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   "source": [
    "# 知识阶段性总结（知识考查）\n",
    "\n",
    "* 本周主要内容：API文档阅读介绍及计算机视觉入门（认知服务）\n",
    "* 20春_API_人工智能与机器学习_week02\n",
    "*  电子讲义设计者：许智超，廖汉腾\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "## 上周回顾：\n",
    "1. Azure（facelist、人脸对比）**考查**\n",
    "    * 有很多人脸图像相关的产品（明星脸相似度，你的评分指标...），你可以设计一款么？\n",
    "    * 你觉得有猫脸识别么？\n",
    "    * 微产品的实现除了好的设计和好的产品，你觉得还有什么重要的影响因素？\n",
    "2. 人脸识别的偏差及API 政治经济学\n",
    "3. Azure computer version 的API调用 **考查**\n",
    "    * 分析远程/本地图像\n",
    "    * 生成缩略图\n",
    "    * Python 提取印刷体文本和手写文本\n",
    "    * 提取印刷体文本 (OCR)\n",
    "    * Python 使用域模型（名人、地标）\n",
    "        * 请思考，域模型中是否含有机器学习呢？如果有，你觉得大概是怎样的一个流程？我们在后期学习机器学习实践后你再回头看看是否现在的答案\n",
    "\n",
    "## 调查问卷（xu+）\n",
    "### 大家是否希望做出来一些实际的产品？例如把我们学到的API放在公众号或者小程序上可以实现具体的应用？\n",
    "![face_similar](http://img.pc841.com/2015/0625/20150625081740452.jpg)\n",
    "![face_similar02](https://mc.qcloudimg.com/static/img/53c6b5fef747c1894f145dff756d5d55/image.png)\n",
    "\n",
    "\n",
    "\n",
    "----\n",
    "\n",
    "\n",
    "<br/>\n",
    "<br/>\n",
    "\n",
    "# 初识地图API（高德）\n",
    "\n",
    "\n",
    "\n",
    "## 本周内容：\n",
    "1. 地图API简介（Web服务）\n",
    "2. 如何选择合适的API（开发支持）\n",
    "![API_type](API_type.png)\n",
    "3. 权衡经济成本和产品设计成本（调用量和并发量思考，经济成本考量）\n",
    "4. 设计地图url需求\n",
    "5. 测试API功能\n",
    "\n",
    "\n",
    "![API_value](API_value.png)"
   ]
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       "/* 本电子讲义使用之CSS */\n",
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    "%%html\n",
    "<style>\n",
    "/* 本电子讲义使用之CSS */\n",
    "div.code_cell {\n",
    "    background-color: #e5f1fe;\n",
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    "\n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 上周补充（Azure computer version）\n",
    "\n",
    "1. 思考每项内容可能用到的场景（至少2个）\n",
    "2. 添加os.vesion的好处？\n",
    "\n",
    "3. Azure computer version：\n",
    "    * Python 提取印刷体文本和手写文本\n",
    "    * 提取印刷体文本 (OCR)\n",
    "    * Python 使用域模型（名人、地标）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-1 Python 提取印刷体文本和手写文本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "401 Client Error: PermissionDenied for url: https://westcentralus.api.cognitive.microsoft.com/vision/v2.1/read/core/asyncBatchAnalyze",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-f8fa05a0ea8f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     25\u001b[0m response = requests.post(\n\u001b[0;32m     26\u001b[0m     text_recognition_url, headers=headers, json=data)\n\u001b[1;32m---> 27\u001b[1;33m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     28\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m \u001b[1;31m# Extracting text requires two API calls: One call to submit the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    939\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    940\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 941\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    942\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    943\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHTTPError\u001b[0m: 401 Client Error: PermissionDenied for url: https://westcentralus.api.cognitive.microsoft.com/vision/v2.1/read/core/asyncBatchAnalyze"
     ]
    }
   ],
   "source": [
    "# A-1 Python 提取印刷体文本和手写文本\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "import time\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Polygon\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "\n",
    "subscription_key =\"764c806a3b0a4187b1e40c084e1e1a8f\"\n",
    "endpoint= \"https://westcentralus.api.cognitive.microsoft.com/\"\n",
    "\n",
    "text_recognition_url = endpoint + \"vision/v2.1/read/core/asyncBatchAnalyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"https://tse2-mm.cn.bing.net/th?id=OIP.v2wFrK0v4dLcZPFkRqLmZwHaDM&pid=Api&rs=1\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(\n",
    "    text_recognition_url, headers=headers, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# Extracting text requires two API calls: One call to submit the\n",
    "# image for processing, the other to retrieve the text found in the image.\n",
    "\n",
    "# Holds the URI used to retrieve the recognized text.\n",
    "operation_url = response.headers[\"Operation-Location\"]\n",
    "\n",
    "# The recognized text isn't immediately available, so poll to wait for completion.\n",
    "analysis = {}\n",
    "poll = True\n",
    "while (poll):\n",
    "    response_final = requests.get(\n",
    "        response.headers[\"Operation-Location\"], headers=headers)\n",
    "    analysis = response_final.json()\n",
    "    print(analysis)\n",
    "    time.sleep(1)\n",
    "    if (\"recognitionResults\" in analysis):\n",
    "        poll = False\n",
    "    if (\"status\" in analysis and analysis['status'] == 'Failed'):\n",
    "        poll = False\n",
    "\n",
    "polygons = []\n",
    "if (\"recognitionResults\" in analysis):\n",
    "    # Extract the recognized text, with bounding boxes.\n",
    "    polygons = [(line[\"boundingBox\"], line[\"text\"])\n",
    "                for line in analysis[\"recognitionResults\"][0][\"lines\"]]\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "plt.figure(figsize=(15, 15))\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image)\n",
    "for polygon in polygons:\n",
    "    vertices = [(polygon[0][i], polygon[0][i+1])\n",
    "                for i in range(0, len(polygon[0]), 2)]\n",
    "    text = polygon[1]\n",
    "    patch = Polygon(vertices, closed=True, fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(vertices[0][0], vertices[0][1], text, fontsize=20, va=\"top\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-2 提取印刷体文本 (OCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A-2 提取印刷体文本 (OCR)\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "subscription_key =\"764c806a3b0a4187b1e40c084e1e1a8f\"\n",
    "endpoint= \"https://westcentralus.api.cognitive.microsoft.com/\"\n",
    "ocr_url = endpoint + \"vision/v2.1/ocr\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://pic28.photophoto.cn/20130817/0017030422594198_b.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "params = {'language': 'unk', 'detectOrientation': 'true'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(ocr_url, headers=headers, params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "analysis = response.json()\n",
    "\n",
    "# Extract the word bounding boxes and text.\n",
    "line_infos = [region[\"lines\"] for region in analysis[\"regions\"]]\n",
    "word_infos = []\n",
    "for line in line_infos:\n",
    "    for word_metadata in line:\n",
    "        for word_info in word_metadata[\"words\"]:\n",
    "            word_infos.append(word_info)\n",
    "print(word_infos)\n",
    "\n",
    "# Display the image and overlay it with the extracted text.\n",
    "plt.figure(figsize=(5, 5))\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "ax = plt.imshow(image, alpha=0.5)\n",
    "for word in word_infos:\n",
    "    bbox = [int(num) for num in word[\"boundingBox\"].split(\",\")]\n",
    "    text = word[\"text\"]\n",
    "    origin = (bbox[0], bbox[1])\n",
    "    patch = Rectangle(origin, bbox[2], bbox[3],\n",
    "                      fill=False, linewidth=2, color='y')\n",
    "    ax.axes.add_patch(patch)\n",
    "    plt.text(origin[0], origin[1], text, fontsize=20, weight=\"bold\", va=\"top\")\n",
    "plt.show()\n",
    "plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-3 (地标)Python 使用域模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A-3 (地标)Python 使用域模型\n",
    "\n",
    "# 是否可以测一测它的正确率？传入1000张地标性图片，看是否正确？\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Add your Computer Vision subscription key and endpoint to your environment variables.\n",
    "subscription_key =\"764c806a3b0a4187b1e40c084e1e1a8f\"\n",
    "endpoint= \"https://westcentralus.api.cognitive.microsoft.com/\"\n",
    "\n",
    "landmark_analyze_url = endpoint + \"vision/v2.1/models/landmarks/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://pic24.nipic.com/20121010/10828344_141947607127_2.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "params = {'model': 'landmarks'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(\n",
    "    landmark_analyze_url, headers=headers, params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The\n",
    "# most relevant landmark for the image is obtained from the 'result' property.\n",
    "analysis = response.json()\n",
    "assert analysis[\"result\"][\"landmarks\"] is not []\n",
    "print(analysis)\n",
    "landmark_name = analysis[\"result\"][\"landmarks\"][0][\"name\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the landmark name.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(landmark_name, size=\"x-large\", y=-0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A-3 (明星)Python 使用域模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "401 Client Error: PermissionDenied for url: https://westcentralus.api.cognitive.microsoft.com/vision/v2.1/models/celebrities/analyze?model=celebrities",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-7ba4fa9edc00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     23\u001b[0m response = requests.post(\n\u001b[0;32m     24\u001b[0m     celebrity_analyze_url, headers=headers, params=params, json=data)\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0mresponse\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     26\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     27\u001b[0m \u001b[1;31m# The 'analysis' object contains various fields that describe the image. The\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\requests\\models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    939\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    940\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 941\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    942\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    943\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHTTPError\u001b[0m: 401 Client Error: PermissionDenied for url: https://westcentralus.api.cognitive.microsoft.com/vision/v2.1/models/celebrities/analyze?model=celebrities"
     ]
    }
   ],
   "source": [
    "# A-3 (明星)Python 使用域模型\n",
    "import requests\n",
    "# If you are using a Jupyter notebook, uncomment the following line.\n",
    "# %matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "\n",
    "# Replace <Subscription Key> with your valid subscription key.\n",
    "subscription_key =\"764c806a3b0a4187b1e40c084e1e1a8f\"\n",
    "endpoint= \"https://westcentralus.api.cognitive.microsoft.com/\"\n",
    "\n",
    "vision_base_url = \"https://westcentralus.api.cognitive.microsoft.com/vision/v2.1/\"\n",
    "\n",
    "celebrity_analyze_url = vision_base_url + \"models/celebrities/analyze\"\n",
    "\n",
    "# Set image_url to the URL of an image that you want to analyze.\n",
    "image_url = \"http://hebei.sinaimg.cn/2014/0904/U7743P1275DT20140904173336.jpg\"\n",
    "\n",
    "headers = {'Ocp-Apim-Subscription-Key': subscription_key}\n",
    "params = {'model': 'celebrities'}\n",
    "data = {'url': image_url}\n",
    "response = requests.post(\n",
    "    celebrity_analyze_url, headers=headers, params=params, json=data)\n",
    "response.raise_for_status()\n",
    "\n",
    "# The 'analysis' object contains various fields that describe the image. The\n",
    "# most relevant celebrity for the image is obtained from the 'result' property.\n",
    "analysis = response.json()\n",
    "assert analysis[\"result\"][\"celebrities\"] is not []\n",
    "print(analysis)\n",
    "celebrity_name = analysis[\"result\"][\"celebrities\"][0][\"name\"].capitalize()\n",
    "\n",
    "# Display the image and overlay it with the celebrity name.\n",
    "image = Image.open(BytesIO(requests.get(image_url).content))\n",
    "plt.imshow(image)\n",
    "plt.axis(\"off\")\n",
    "_ = plt.title(celebrity_name, size=\"x-large\", y=-0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "key_xu =\"1a8b4a8f8eacf6e72af8287289e0e270\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# 初识地图API（高德）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 地理编码\n",
    "\n",
    "* 什么是地理编码？\n",
    "* 地理编码的好处是什么？\n",
    "\n",
    "----\n",
    "1. 将详细的结构化地址转换为高德经纬度坐标\n",
    "2. 且支持对地标性名胜景区、建筑物名称解析为高德经纬度坐标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# df = pd.json_normalize 不要忘记我们学过的这个pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B-1 获取地理编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# B-1 地理编码\n",
    "def geocode(address,city=None,batch=None,sig=None)->dict:\n",
    "    \"\"\"获取地理编码\"\"\"\n",
    "    url = 'https://restapi.amap.com/v3/geocode/geo?parameters'\n",
    "    params={\n",
    "        'key': key_xu,\n",
    "        'address':address,\n",
    "        'city':city,\n",
    "        'batch':batch,\n",
    "        'sig':sig,\n",
    "        'output':'json'\n",
    "    }\n",
    "    response = requests.get(url,params=params)\n",
    "    data = response.json()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': '1',\n",
       " 'info': 'OK',\n",
       " 'infocode': '10000',\n",
       " 'count': '1',\n",
       " 'geocodes': [{'formatted_address': '广东省广州市从化区中山大学南方学院',\n",
       "   'country': '中国',\n",
       "   'province': '广东省',\n",
       "   'citycode': '020',\n",
       "   'city': '广州市',\n",
       "   'district': '从化区',\n",
       "   'township': [],\n",
       "   'neighborhood': {'name': [], 'type': []},\n",
       "   'building': {'name': [], 'type': []},\n",
       "   'adcode': '440117',\n",
       "   'street': [],\n",
       "   'number': [],\n",
       "   'location': '113.679287,23.632575',\n",
       "   'level': '兴趣点'}]}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "中大南方 = geocode('广东省广州市从化区中山大学南方学院')\n",
    "中大南方"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## B-2 表格化地理编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formatted_address</th>\n",
       "      <th>country</th>\n",
       "      <th>province</th>\n",
       "      <th>citycode</th>\n",
       "      <th>city</th>\n",
       "      <th>district</th>\n",
       "      <th>township</th>\n",
       "      <th>adcode</th>\n",
       "      <th>street</th>\n",
       "      <th>number</th>\n",
       "      <th>location</th>\n",
       "      <th>level</th>\n",
       "      <th>neighborhood.name</th>\n",
       "      <th>neighborhood.type</th>\n",
       "      <th>building.name</th>\n",
       "      <th>building.type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>广东省广州市从化区中山大学南方学院</td>\n",
       "      <td>中国</td>\n",
       "      <td>广东省</td>\n",
       "      <td>020</td>\n",
       "      <td>广州市</td>\n",
       "      <td>从化区</td>\n",
       "      <td>[]</td>\n",
       "      <td>440117</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>113.679287,23.632575</td>\n",
       "      <td>兴趣点</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   formatted_address country province citycode city district township  adcode  \\\n",
       "0  广东省广州市从化区中山大学南方学院      中国      广东省      020  广州市      从化区       []  440117   \n",
       "\n",
       "  street number              location level neighborhood.name  \\\n",
       "0     []     []  113.679287,23.632575   兴趣点                []   \n",
       "\n",
       "  neighborhood.type building.name building.type  \n",
       "0                []            []            []  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# B-2 表格化地理编码\n",
    "df = pd.json_normalize(中大南方['geocodes'])\n",
    "df"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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m6XkbkDjygms9qTUbGro2sLD7IolhNbecUtGE/vHj2ws3dMzKaH+VG9Bw31VotjLVHnVfgd1Xm5Jlt0DZrGG63Pe6C310zMuxKQv2/PbuoRoWbo2PwRDo7Xv9Wi+1ay/W7yfHX6c5tvzg3kO6ccWR7Iah9AgggMB5K9BXD8yz4E/SzW+N5VJuuWiAinKjMEHrCb3qhwO05o16rT7WTz+7fogKfd8nzlob1n6kf8wg+JNTMlT/65q++s1vPtG6DR/rvrDd9P93Q2S8xvlpUvV27vK0kyKgcztibQQQQAABBBBAAAEEEEAAgdMW6K2ii/t6vyHH5FkmiJ2Hdd0LvfXvtw7WwZ11unlts575vu9HpgVxflndmQOf1P1PfKLg/IFasfyAducM0u8T0pu3avmyD7W0JrbP4UP0exsPPRo3sGVLbFl9Z47X/df1aXb/wqYqYVHJYM2+xMkdb0EVG034d8krnbAf/zaYU8LU2wI+gYDG5Peyf/HePLIK7zerWzXH8mJV7Aippv8FunfGBar4L6uMiF346g+O692xVjtigaOS4v6pU845lSfHTqiiPmzjDQUU+uiUvny5exxb1tSkmx/5OBpcyrGeFf8Q61kRbtadNpLx7oTC8gSBMyxgH3z/XObLo+nsPuAfE6uXgvbJ0M+COr5XbZtCBHMTawnTjavVZkNmIIAAAgh0Q4ETuu83R/X7mwfFPvtPacubn+i+TY1WVusVfc9FKnMH1OzdR7NnXKjZyWdhDWyWr/hIS6s7DrwMGz9Mq24cFLnJvvw7ozTmhX1auqnB2+OadQdUMzTxe8ZZGGrpo4XWS6k0dgfbbGnp/nGdpeMdmLxuqyp2ZtYDyTsoDxBAAAEEEEAAAQQQQAABBM6gQB+VWmrvPTUn4o3MnbHDkyMS1Yf014/Z78GjNlbP5IsSGq1X7zjahQ4aNibQ8uPR82g5qsV/yNWiqy+I/P6s33UoHvxx1rCOG/7J6VTS06Zk7qw7v/KpeZp3sb/yOpNT6KuFM1Lni3e3LrvS0snFnsy5+JT+2rqLRaaaw/rmU9Z1bPKFer7Y5iT19oltYvnxLZp52NJqWSvYNTX99dsf+yomcvpojK140Fk511d2C0AxfoonyIOzJHDvrUO9bpVn6RDsFgEEEEAgCwVaKg/qzjf76smy3jY2T43WeK2eGnX3E/v15PdHtptqd0WGwR/Jehv9TTT4E2UKaP6tl2jYbz/SQzujXesPWqOedW6LrATLvpoXHur9YGg5fkJbrJcSEwIIIIAAAggggAACCCCAQPcSGFaWr+dnRccbDx1v0eInDsQDQclFteCP1Ec/muofnzysFduaNGXyUM274gK17KqPNVJM3riPiob76t69xa2qqT+pdZstuNTYqoW27y0fntLMK4New0fl5sj6IcWm3ir7YlA1tnpCo3iLoNRUW/ak+sRgkbtVd/+b9QEgJ82b5AuiuOLpAjPu8lR/M9xm3o0X697xsR4U9toKHW9VcEDbF1nhxRfohwvGaI6lpiv0H693X5UXWZ7DaqlobG48tVYHPS78u+AxAl0RKLWcl/MKU7xf0u6sVevW1ul3R60jXU7ia9xJY1g+PV+zMxikO+3uWYAAAggg0K0Edm/6WF/aFC1SzvD+mn35BfpyyQBdPsLpbd3+NO/WIgvOtGr33kZt2W29sittoM7ITXzydtbbaEWDVs3LU/wrpLdm3zjKep/u0/3vdCK/ctbfySbb8BwBBBBAAAEEEEAAAQQQ6BkCUy+JZyAKDuilgy2tvmBL23N0AkaR8YFii+qrjmvmgiLZyC7RaWieSi1LRWL2rD762X2j247xE9nilJY/Vq3fXJGvB66NhnTmzMrXnNjuUv0pu3q41ynEv3z3H5r1jQ3Z2T0o6382hyJj+PRRyAJBlm1NLU63LUtNUjgksbLauWChQ2EFh9pK4ZOqPmTrD7T17MXnTc4mTWGrrJByLPQXif5ZUOaYOyZPzkA98518Xe7PKHc4pPv/o48emeWMXmVT+ITWVfXSzJIY7eFG1eT2U2l0aez/Xppyua1vLVb79Y+Xs/lQU9ILOGEjniBwegI2ts+TCTkvM9ldb137V0NVZONotZ16qTBec9d2MXMQQAABBLJGYNjwHJVelKsJo/rp8kK7bxnRN5IONO0J2P1STVPvtvdbdt9UWnxB5N98u7GWjW1Yc+iE3Xe1aHd9i3636aiqbact1Yd140MteuZ7F8q5JYpOdv90/cXaXfWhVti9GBMCCCCAAAIIIIAAAggggEC2CvTS5Rf5Qg+HmyPjj89Pezo5eqDc3/unVa+8clA7pg9Q2fjYfgL9dO/kvrp7qz/dt41b7jtM4u5PRbNtnYiPY564PPNnLU7quiyd0vJky/ms+e1+rfEVtqxsmH5YPsg3J/qwwvLYL9cgqwC3U+7dS/VVh3T3+mNaOP8Szb84HoSR9XKo39ug+9YebdslraUlMUVb6wkbVKpeW6680He8sB5b9bFWTB6hp2f01yv/eUIz7W/yVPi5C2zWcZUVxSOhLRYFZULgrAkcPaEae4mV+l7umRyr35C+KnXHfchkA9ZBAAEEEMguAWsgsOqu2HiEHZS8/kCjVm8+pOU7oy2fcgoHalH5YJUX5SiY6vvFxosrtICS82+K5c2t/6MFgLzgznF98xeWWu4+Sy0Xa9EV+igkG4qonSnprpt7p3asWIQAAggggAACCCCAAAIIfFYC/TXBV59Yf6Cp3YKUzRqhMl+ni+aao1rq/DZ87YjqbQzZ4bGty/5yiHK21iXW22d9hKNdmtNe2CN4cgb117xpgzVnfH8NTz6j1pNa/Vq96kuH6Um3V05vy+c3ebjmbzumpcs/VGjeaN1bHEtt4iyzINKmsqGqrjqmFZuPaE21G1Vs0d3/FtK2m4ORnj4PPfmR1jkDI8/wmq6q+ejJSEq33VsP6K+32vUpHKrrU12mgQM0x8YCKvMNcHzwI0YASkXFvDMlcFwb9raqtKi3QpUNkYDovSUdp/Q5U0dnPwgggAAC3VTg6BGtqB6ihfb9kHo6pWrrtfy7/z6udy1/co51uZ453s2SbKlCLSC04Y8BlY0ZoHJLF1cYvy1K2F29ffesbhPcsXurRz7Wk4suUmn9EX1p+aHYNn208GvW69rZV0L7GOth5B7aFgWL8/TkzbaS//7PTqPmvw/poYrs7J6fgMYTBBBAAAEEEEAAAQQQQCAbBYr6y/8Ts/oDZ+zWNL85rVHiP5fFO0k4PwJ/9W82EI8ztRzV6uq8+O/VIQO00MZaebwmuliyFONP7o0MvxJyfjxamrhVtw6OD7livx8Pbv1Y12yPZQGz7GFjykfq+atjPyzDzbr7pzUK2bAZz7uZk2LzduT4Moc5WceydPL/XM6+UxgU1PN3DlOpP42b7yxqrBXp8v8Ka/b0CzUnqTJiy9r92nLFRdp2ba42rD+g+/YG9bNrB/gGeOqlouKgfuj800k9/tjeaDqSShtsav0JHdx+RLutXmHKrGG+6GSrHn+6LpLaxCtGzSF9Y32OVlkvoJqdB7VuSF6sx1Fv/e31w5TjRTZPqeJPjd5mPEDgbAis+e9mLbxIunnVYX3ZPgw7nlq14oWPtNQ+VIPeazW6VchSI94xf3RiD7qOd8gaCCCAAALdUGC59Xyed9cQBW0Mn/qWXhqecG9l90RFF9gNt9N7uXNTqMbJz2w9SYda7mU7RuqpUXcv3pO0qLcuL+mvsg7vVK3hjq2XPDXnXEAAKBmF5wgggAACCCCAAAIIIIDAORIo+4L/d1qrtlQ6Y70mB4CiQZUf3jrU6+HjFK9+1yEtrY8XdPkmy+JV5Gb86q2ZfxXU4/8a8lZosQ4Z1d6zhBaE3tzIsDGxZ8MG+sthv4Ftfsjt/+Gs4/wutj/+bZzZ2Tp1+LO6W5+YXVzl+iJxscI2Hw/r7UoLptjgxT+8PvkUW7Xmtx9pdV6+F9Urn3GhptQc0V8/+LEemD9cMy9O2qapRRWxOot5t16ie53w5YyhKWh6a9E/jNGihCWntPqFD3V/xTDpjaOqmDjAKsyjb4Cisb6KFEsnt6E6YUOeIHDGBQ5WfKxJFZ3ZbW/N+7uLNdOCnf2SN7PP02DCB2byCjxHAAEEEMgagfoGfennh+0O18ZILLtIm2b5b9a7fha736uz/MzOjX77U07JMP32+v5603oTPbb1eGJ3/vY3Tb20NXtbZ6U+IeYigAACCCCAAAIIIIAAAtkiYOO7Fvt69DQ1a3OK9oDNja0qst44s/3V7OEW/Q9fcCdyxjaG7JbjgzQl1sFjeElQpQpZY8OuTWWF8YxIzQeaI8GjIv+ukkID/kXZ+Ngf7srC8h/XQ1vi6T2aj7VoQ2WLWiw1yZQrg5qSFMgJHfhU33jwQ+0uLfSCP+5Jv/3HRs272YI/llCwovK4KmriOebf3Xqoyy8oqZdKx/TVurX1WmdFPbj1mLweau7B7W9zjR3T95yHCHQbARvQe/gAG1At+R/Bn25ziSgIAgggcEYEYl3aC5NudkMHWlR94ET0X80J1Sekbm5VdU18+e4DYYV8Da769Y3fWLdXxjumDrTvmr6aY41yNlk6uGHWjf/dvWHVHwur5rD/X1Jgx1pmJS63dY9Z66/DvkK0d2CWIYAAAggggAACCCCAAAIInFmBHKuX9wV1nMwQ1SmOMPzSfD3jpmKLLd+w9uNIHXlO4SDr2JGnsuFO54+TWrojHgNQoJ/mjW/bKSTFIdrOGp6n2SPi2x6035tpp+FBPXDjUE2JlCHtWt1+QdJP/G5f3jYF3L2hTiuGDFLNfx2xfIBOX63+ev77lkfev2bTCa1Ye0ArDuXq598bE80n71u+4ZW9Wpqbr1WxMYLKnL+Hj+krDx7RbEvdtmGT7wXm2y7jhwmvo2NaXTXMxhxKjL29++6xjHfHiggggAACCCCAwDkRaDquLz31ScKhZt58iR4oid7HhKoO6eYV8a73zoqPLBqjKUlpQxN2kPwkZ5Cuv9h3X2Q9uZ09Ll2xT0uT17WGNY98r8hr+RX66LBufOFIm7WYgQACCCCAAAIIIIAAAggg8NkIFE0akJDS7d33Utd7FyWl83Z+X/7jO9EMEoWXDtTsK/vZvyFqPhTSl548qvrJw739TvkLSwm3s7O/BXvpgb+z1Oc+lop26uSLvhC08W/72b/BVoZjuvHJOh30bZstD7M+AGS5SvT4b52sfO7UqPs3NEbG3AkdatSabce0pamP5k8t0OsjfJULkdVPavmyvfrliBHaZIEe/7Rh0yG7oLZ8fWLwZ8ULe/UbnVLwyhF6/fp4CreKN/fr7ligqPQap4eRmzCr1QZGTtrHupDuvcs3/ooNLLW8wp9o0F8SHiPwWQrYGEDLPowMrJbjH/jMiuTkwZw//xIt9FfafZZF5dgIIIAAAmdewAYrXDW/MBKQcXc+bHj8fipYPFTP3Gq3z94dpY0XFBtL012/o79TZg72buKddSvePtJOCriAcuKHt+PGW251dByWI4AAAggggAACCCCAAAIInG2BXrrjSn89e1i/iwV12j3y8UZ9c4UvoHMintWh36CABW3q9eah4ZoT61kUHD1QZTrSYUYtt4ZeNjbtDxcUauYQXymOf6qllUlZJpzF1pnDaZQYshR17tRiDRWzMfjjlN/7ue6eTE/4W731oB7SBSotyFV5eb7mpWiFGjpwTN98qk79rrE899f6X5Q20FTlQS/a2NbjlFVK9NJ3yuPBH+dVscLtJVSYpye94I+ldvvoqFYcTdzLsOSakUCfSGVJRWKcKHEjniHwmQjYGEALxmhe/PMusRT+SrjEJTxDAAEEEOgJAr17q+ji+C1z21PqrcuL2lvedovEOf21cILvdtTGRFyxlUYxiUY8QwABBBBAAAEEEEAAAQSyQyBnfH5CkKW55pjWeUVPV8EY1uNPf6xqbz174BtCSLKhKWzWb3Y0ao5bj987R/Ou7KOK5OBSU2JAp9nq23NK8rTqa0OUmO78lNa84uvR4z9erpNibqCCl/t+64YT9+svand/7PvF3d2Lmkn5+uhn94xWuT+Sl7zZ8RNa/kqtllbn6IEFl2hmYWINdqjmiG5clRSxSdpHablFCwf6Zwb0yI+LVHPopIYNDSj+0mjVr15p8K9or7hBeqbNwMoBLfpWvtY8UddOi9fE3fAMgXMqkPg2OaeH5mAIIIAAAj1XoPSaPJX6vmNCfz6qLT33dDkzBBBAAAEEEEAAAQQQQKBHC7RUHtaKKgugFEfDDlu2HfbOt7RsmErbdNRo1epVH7XpQDHV39DwULNqbC8t246r3gJAw+1x87EW7Ugx9uu864MJKd76jR6i129MTPvmFKjizRo95Ov9U9MQTT3nLHMCTvNuzI8+jP1fXX084Xk2PelhASAbEOo/G1WelM7NuSBOOrgVG49ow6E+umNmobbd3PbUa3Yd0o3/6utqluZKNtd9qooDfVQ2wj+wcS8VWvDHP9XsrNNSf3Y65ejJe4ap0L+S+3jIQK26ubnD4JO7On8ROFcCzTbAdkvCOFbxI+dYHp5+voq7+BIeIYAAAgj0CIFwix56rk41OdF7ntCxk5r3dyO9Fl3NNUd157+FFBzoLD+p+pZcPbIgzb1OG5C+undyvNmM1Ko1m9pvhOOskzBlcSushPPgCQIIIIAAAggggAACCCDQEwRabLgWZzzXkiF6ZFJAi3c6PWf6aOG8Qs2PBYXip2nBnxc+1OLq+JzooxxNuShe4RhqORntNNES0q/e6aec3Ye1vLJt5oh7baiKeQlDVfTW/FkXaEt1WFOK4vX2FW9+7A3l4h65ZWeTam4cmLre3lbavS97U3fFz9w92yz/W731Yz1+yWjd+7leqvm4WVuqjmvD7iYNKxyovy0foYVD4i+e+KlahcPaWj2UYQ626p2Hdbf9GzZ+qFbdODghqhjfp1Q4Nl/PWC7Cn/z2sHVh66uf3T1SZQPiazRb9LJ2QD8VxSKfhSXDtOr6Vt38SuqBseJb8giBsyOQY+M2tAz0j6dwSqufi44BlOqIiWMARbdzxgpyxgdiQgABBBDoCQKnVFNjLavss92Z7F5ee5xWVu79VOCU9tTbzKPu8j6qabJ7oDatutpaFE0epjL/eoct/7LTrMu+jOZPs+72bTaxNLzhPgnd9nOGWAq5a3orJ9f/3eVu2Kp1Gw9rd/bep7snwl8EEEAAAQQQQAABBBBAIKsEnJ5Ad1dKRVcOs2xYgxJ+x3kn0tqqnM/n6d7P+37PWSP00rHBhN+KNR81xzY5pRWWti3VNPvGUUnBn9hauRZMujhWT2kpxx9fbr2NnN+dbaaQblwiPXL9YBUN8JXHGiFWvH0oRZCqzQ667Yxera2tXaqpnfyT6m57Uk7Bcgb1VXnJQH358oH64sX+tGyJxa7/KKT/sby+wwGjolv1Utn4QZo57gJNGd1Pw/2VFom7bfOs5tgpFfor1ltbdN9D+7WlcKj+fcFgX9o4qabqsA161ZC1A0u1OXlmqOgii6zYVP1x96mFmnfrJbq3KBoQra8KqXqEfbgmpDbs6oVr1fIlHyb1fuvqvtgOAQQQ6NkC3fH7oWjyRVqVojd1V67Eu2/u1zfdcRITdtBXTy66OOGmfssre3Wf5W8uuqZQq6719wxK2LATT07Z91E130edEGNVBBBAAAEEEEAAAQQQyF6B7vT7sqhksBbNGKKyoak6Y3TG2Bqnv1DdbgDGGXdok/XeiU8nteGdFk25sn9CnbuzvNmySMQbrp9Ssy/JRIuTYcJ97hY70EvBgI1BlHtSjz+2r02quvgxz/2jrT8qyuigPasHUM5APXJrni63VGzBDoIz9TXHtfyNeq2u9uf368Bs0CD97MahKVqkRrerqQ5p6X+d0JenDtGUEe6rJLosIfhjKVIef8qCP86imkO6880cPe8OYGWzCouH6IHJx3Q3gyBH8fg/6wTcuHzWFZwCI4AAAgicE4GyGSMSgj9qatRj7uCdJ9w77tMtit3Mn+4u2B4BBBBAAAEEEEAAAQQQQKCTAn30nb8ZmvibL7aHkGWM6KjePuFglinil9UJc5Ke9NUjs/zBH2nLK/v1j07jwtoRet5SwPmbF/azgI7zz53aZp5wl7T9W29lz8YpMUqRjWfgL7PlAwyOSB/8aT4e1paKBt392B5dt+yTzgV/nOMcPaLlVcmVEq3aXXlU9y3ZoxtfqNe6nUd031Mf6iurGvSuMzbU4ZAmPWi5871yhrV02V6tqPdmaPemj3WfP/3coZDuI/gTB+LRWReIjONzxj4N7IP0rJeYAyCAAAIInDMB59Ynw3/+1lPtlq/phOp948u9u/WQpctlQgABBBBAAAEEEEAAAQQQyH6Bk7pvxRGF/CdyrFmPW0+eLy22LA27mhVqsgZ71uMm9T+np06r3t11WF95oq6DLFkntGZnvOlf6KOGSGYJ59DVFQf018sOquJQcn2+v2AZPj7cqHXdJ7FThoWOrtazegCpUd98+rB+f9cQr5dO/aEWvVv1qdb85xFtqe9StrsE0BW/OaTZ3xmqHAv5rXs3pF9WHI8OQpWwlnTQ8hx+8xeHvbn/+IdBenqi9NDTNVqXYnzjLWv36+5jF+nJa/tqsQWSsvT15J0vD7JAwPfZF7IP4fv/7ajGDI0O8n16pT+pHb4A5+nti60RQAABBD4rASeY0/LxUX3Fbpgzvy+x1G7fs9RuOXbPZa2qmk+kvveq2HRA122SysqGaeHlvbXYlybOGc9x0tbP6qw5LgIIIIAAAggggAACCCCAwGkLWNar+98ZoEfG99Lq9fVaXNHo7XL5v9Zouffs9B+sW1ujaqtXf97q1R9fEa+Pj+y55qjuftKpjO+l0qL+KhoS0PCgpXSz36s5sZ5AXo8gZ16a4hysTVGhn2bd7ja7R44BVFQS1ARL+vG7ypZOVFh0t0tDeXqSQHfKwdmTXDkXBBBAINsF+H7I9itI+RFAAAEEEEAAAQQQQACB7iHA78vucR3OVSnOzzGAYrrVlTag/bmS5jgIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDcTOGOjfnSz86I4CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMB5K0AA6Ly99Jw4AggggAACCCCAAAIIIIAAAggggAACCCCAAAII9FQBAkA99cpyXggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIHDeChAAOm8vPSeOAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACPVWAAFBPvbKcFwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJy3AgSAzttLz4kjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBATxUgANRTryznhQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAuetAAGg8/bSc+IIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDQUwUIAPXUK8t5IYAAAggggAACCCCAAAIIIIAAAggggAACCCCAwHkrQADovL30nDgCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgj0VAECQD31ynJeCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggcN4KEAA6by89J44AAggggAACCCCAAAIIIIAAAggggAACCCCAAAI9VYAAUE+9spwXAggggAACCCCAAAIIIIAAAggggAACCCCAAAIInLcCBIDO20vPiSOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBPFSAA1FOvLOeFAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC560AAaDz9tJz4ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIINBTBQgA9dQry3khgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIDAeStAAOi8vfScOAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCPRUAQJAPfXKcl4IIIAAAggggAACCCCAAAIIIIAAAggggAACCCBw3goQADpvLz0njgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAj1VgABQT72ynBcCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgictwIEgM7bS8+JI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAQE8VIADUU68s54UAAggggAACCCCAAAIIIIAAAggggAACCCCAAALnrQABoPP20nPiCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg0FMFCAD11CvLeSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggMB5KxDo6pm/+HcnI5uOvmRMV3fBdgicNwJ7P9wTOVfeL+fNJedEEUAAgYwE+H7IiImVEEAAAQQQQAABBBBAAAEEOhDg92UHQD1ksXudMz0degBlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCBAAypILRTERQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgUwFCABlKsV6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECWCASypJwUEwEEEEAAAQQQQACBHiwQ0uaVL2lPOKyBY67VDVMvTXuuTZ98oKrjQY0dMyLtOuduQaNq9+xVbU2NGgeP09TxZ79MTQ0HdCI4QsEMfsmEw40Kh/sqNzeDlbuC1tSgPdU1qvukRgMvu1pjL+zf6b007duqlet3K6Cgpv7tTRoT7PQuzuIGYe3ZXqE69Vf+yBKN6cL5ncXCsWsEEECg2wic8+9m+/5pOBFUXmZfhmoKn1Agt79915yN6dzfC5yNs1Bro/bsqlKj7TyveJwKBqQ5SmuDdlV8oP4FhRoybITyBpxZ1YZ9H6juUyvD6EuVn64MaYrWnWY774ld+xuUm1+qsaO61c2Njyms2j9/oFA4oNEllyqXbhI+Gx72JIEz+ynVk2Q4FwQQQAABBBBAAAEEzpFA3eZfa93ODyJHm1R2UztHDevtl5Zp3X5nlVLd9U+3qcC7ow8rFHKqLTKdLOQQ7HzAInHvIa1b9pyqIjOrVPzAAhWc1R/PDXr5F49qlx2veOo83TJrXDuVWY3a+MiD2nhEKpjxbd01rTCx6GfiWWi3lr+4JrqnXf11/11Xt1Oe1AfMHZKnuqrdCtniyt8U6v47Jnd6H6n3fAbmhmv06ktrLAAkBSYt0P3Xpw9MnoGjsQsEEEAgSwXO/XfzrpcXa+VOKVh8jW6/fZby2/nu3bP+X7R88wFp5Fe69D3V8UU5x/cC4Q/0xD8ti3w35U9doHtmdeG7qTWkHRvWa+P2Gk36+wW6aqTdD7XUaPXKFZHv40m3/1jXfc7mNVTogV+8pJLyebph+jjlOhith/Tampci6xXf9F3dPvEMNn4J79WLT0XPLWjntqgr59bxBTsHazRq8/PLIvdgkfuH5ACQNXgKd6YUvQMKJLzGw9q1dqlWbpbm3LNAE7rYQKVp3wYteXaDlSSo+T/6vsZELnBnCsa6CGSHgPdzMTuKSykRQOB0BU61nlL4ZFitJ1tPd1dsjwACCGSlQO8+vRXoE1Cv3r2ysvwUugcKWEXG6rXR4I/yZ2j6ZXnpT/L4B9oSCf7YKiOtZajvbr5241ItWV+TftvkJf2u0Q9+PCtamZG8zJ6H6g6oqaPbhZy+umqiVLXd2cEH2mgtYmd+PqhwS4od+mYFLxyR9ri+1do8DO9/JxL8cRbUNVplQJs1EmcEcqLPw8c7Exhztglbr55DiTtL8SwwYLTs9BU5/f2btG1PicYOtK3bc+ttvWnyfS1hg6WaW16opzfYtatao837rtC0UacbmEtR2K7Mst5WDbHtRl/oK3NX9sU2CCCAQE8VOIffzRFCCxJstOCPM4X2n+iw10IgJ/Ztecx6xdo2HX13RnYc+6873gs4RevMOfjPx3vcO6yqLdbDtVl69Vd/0IRFM+R0Frav8EhgJ9Anuub2116JmO3asFszYwGgcO3eyDrOGgV5Z/i70XpqudPAAV2/Fwjv/4MeWPK6u6sz/9eCiT+wRi/txUvce7C8NherUW/EGuhkWrDkYFjdW89Z8Cd6z7v6iQdVd8ui9u+fOzxQ16073DUrINANBNq8DbtBmSgCAgicJQEn+NPS0kGNzFk6NrtFAAEEuouAEwBvOdminJwcgkCxixJu+ECbt++NPMsfP7lLaaw+i+vb9Mlubd7p/PjrrwlTJyu/vV+hn0UBMzxm7ea1qo2tO3X25HZ/TNf+1x+8SoepM76YUAHSdNzpQ9KJqV2vRm1Z+qg2W8VIZ6Zda5Z5AZr02/W1Vpb/3KVWlpWb3ozttq9mlpemP0Rkie/HvAWqOjU1fahnn1jmWWe2rdMC+lGt63DlyfrBT2YnXOdRU6319oZoi983Xn1bU7vQk6jDw3ZhhaYjNV4L3VEF6Su59m1cpqfX71WgXzsHaT6h4lnf1i1TM+uJVffn3aprCqt/XrHGOC2zOzMdr9GuKid0latRYy9VMKHVcHxHYWuB3JkpEOjCz+dMWjm3admcVKqmkGotIBv6NFrevv2CGmKph/LafQ/H99EUOmABzQY1xk63bzBPBQUWhE3jEt/SHlkaRSfFYUMo2lq7v3NsC+B2NeVRJE1WXZPtuP1rk1AG74mlZ9oZTc/UpdeFt5+uPXBfk5bPSGM/10GL/9O8Zp0tYafK5tu5u117np19n6i913OyywVBDc+3lJ4Zpuk8rdey77yb9lsq1QZ7HWZyLX3bpXt47r6boyVo2LnJu28Ye/011neh/cn76LJAUOc+xbrnvUD7Z5vp0jx99Y4Z2r5kvXRkgzW+KNf0kU7zj+gUCNh9w771evn9aEBm0u0z5DbPCTc7n2HOFDzzqVHtArnXqNZ6br18dLJ0wi1V9KjJ/zdZOaZ9ZYYKfN8J4ZZO3hMm77Sj5/Ud7T/+vR1oE8jq5D2ZlcV3apGS5V91m245uFQvxoJAG19crNCc7+qGKzv4bO7ovFiOQA8VcD9XeujpcVoIIOAXcHr+MCGAAAIIRAWcz8S+vTv/A6Qn+oUPV+qNDZsipxY8PlpjsyTNU+32l7Rxc/QHaNMlV0RTdZyBC+RUUO61umPn1THwrOdfP6A33F47/cp01Zj4D+a2p9KgbW5PIWddJzWJbyq4eoHumpD+uz5gOep3PP+oNjr5vGzKH1/S5gd1dEn0/9xB9je2rn/+6T/Ol1cZ1ZmdNX2gN3bGWsaOnKWxbk2M7WPPZnst7AlZ613/z5uw9sXKX7fBKgnqktLFORXzgUJNvyGx0iRSJNuN2wq4M0XMaN38oV7ljrd+7qWaOTGoF7fb63n/69ped7WuyveWfmYP6j6MJveTClVckPh6SyiUBQmshkrhDgKGDUcz7InVtNtSsjwXbanupJ6z3m6dmfa88ZRWbnNeK9GULsHkmiNb0rRnrR5eFv3cy3jf9r6754c3tZtqyb+vzFtgX2rpHC19ov/l6+zIxpnY/PIKrbMURamm4GXlun3ujISegAnrWXD/5eeft9dT7H2TsFCRlEZz3JRGScucNvDbX/m1Xt4W652YtDxYPFlz/nZ258assvfw0xZYjb4t01+bpEN5T53runyDpbGyKZJSqJOvC29HXXjQtGe9noikCbKN2+s9ebrX7GyWLXnfmbzP7Jot+Yl7zZJ3kO55UDd85/ua6P8MCx/QxtW/ss/w6PVL3tJJYzZ37iyNSjfeyWm9lpOPZr0PfrlM25zPq/auZfJmaZ+f2+9m2Qg129e/FyvNpZo2If5l2LTnD1q94QM53/f+qWFf7DOkbr2WvFiT9Jlh34XHA5pw3U0p02h1u3sB/4md5uPAyGs0c+T6SFrdja9WaPq3Rnvfz4HmD7TSfc9bb5fpn4uH2eo+dD8X+yv8aaPccFCb4tiYSxkF2ttsGJ+xfdvW+JN2HgUnXJ1wH5w7pETTZ+Ta/VYGv3X65qrpbbuP2h87wOBSTZ9yqWfR9rD2fd83bhVZ7nz2vfa66k5E7xX62nfIttg9WEIgy9ojF06aoWkLv6uSwybX276gG7bq6ZXR8yyZdZtmFgUjPdADvRu17VfLtP1I2xI4YbISa1Ry17AVWrIm+n7YvvpRW7FzQaCEd0rCk1THZB4C2SvAyzt7rx0lR6DTAqR96zQZGyCAQA8WiHwmZvCbqAcTxE/NV2meYSPc+Laf4aNAjvNjPBoAclN1nIni1G7/tbUojO7XywF/JnacYh9Nf96qytj8gv9jste6NMWqavrzpmiqMVtYct3VbdbNDY5QO5005FRGb/YCOoW6YUaGlerFs7Vo7hWRFLKRHw9NB7RjR6WOBfI1YUKpBib/onBiUAFrqb/9bdUet8cDCjVxYrFybb7ThyDQP0+ZjFmdbFD79ptePGraV6/wVUw0RtK4VKWsIIjvpXKnW2EWnyftVl5ZYqWJf6nzuHj2Qs0dn6dGCxo7baebDldph/U8Cwwr1oQvFPrK4dvSjLZXVEYqhXILSjTx0hHRVsUWdOo/OC/lNsVTr7FcctF0LRs37dZVN3XUw8l3vDP0sKmhRrWH7TztmgZ6n7DzdCtra1RnFYiBgC/A6AwoPrBQBfn+wFBfjZ16tfJz4gUK2Jnv2mKt1TsIDsW3kHatXxv1spltU8f410zxuOE9rY4Ef5xl/rIlrev73Eta0uap8xKPnHnzbh2zyqtMexuGLIVeZpOlW4y8b3xrtx7Qyw89qu3tuIXe32DjYNToThsLbFTy+7Duj1r82Kp2e7FVblihhz+0ND53JKXxsWO/ZmNtbWvnPRWq2qrlP39bX73rxzZ2RvLBfefhPbTxIJb7AwntXBtvm/gD5/PLDf44czv9uojvqvOPLN3WymXOGBGxKd0X5eleM3f/nfmbadlS7DPT91nayu2kfTo9AKNBYOuxdsSCve5ngwWRnrUgkhtOTtos8jRUtUlPP/yB5v/g2xqTHAQ6nddyioM17FwfDf44y9JdyxTbpZt1rr+bw/vfjoyr4pSnYIY1YPD15Kt9f5Mqq6L3L+nKW/f+e953qX+dqrcma0J7DYC6yb2Av8yn/bjVAl8zrtEbvz2kG75q37lNh7zvnmNNeZo25xpVrt6tG+ZY72wntWvEKbqfpAAAQABJREFUOqzaD92bqQNa+diDaYtx2veQ/UaoeGQ88JTuQI02/qP7dvPWybPg4LQM7/Nso+2V6+3/6Gtn6pyva1q7DZK8o8QftBzSlm3vpf3O8Qeytu+0nvs/sYBv7NSaWtwfZH01YWyp8r2YZlh5vvuJ+MHijwqumqd7Aiv0xEvxIFDhyO8r8MYyvbqv0bZv/7umqcVNdHtALzzyaPR4LY2W/rZQty68rXONHOLF4hEC3U4gkzu1bldoCoQAAggggAACCCCAwNkSOFuBpbblDWvHv7stOy19x4T2UmNZ5elr7rqlmhlLcdG0zypF1x/Q1PKrNWFMO2kvbHyCpy0XvFt9P9Z+3LepMG5bwMic/EsKFfSn7+hvwQ3rMRap/ij+sWamHK+mv+re2qTNTgVyv3KrhLii3d5GaQ4dn20Vq2+4vZ/yyzU1dsy6nZa/3wIquZeWqtiCTf4fNwFZGrD3YxVhgwtVkiI6Fra0Vu11bHEKMKogX7l2/tZGNTL1P2wt2Tc7PUd2a/TE72qM/6CxdWRGO2wdxyg4tUTTruy4Aidw4RWa1O/1SMVkaPtW1d5QmlCx5+76bP6t3fqcDRSeuvLw1WX/0ubQkV4YCRWG+Zo6c4ZG+SokIxs56Yrc+rI2e4nPCB8/oG2/+5X1eMk0cBLf1gnR1FlAdeWzr6etgPKvHcgZobHFVuGX1FLev06upafaZ62v40UfrYEdVEb5tz+8b2/sqQXGyq+NvOfc96B/vXC4f5tA6p51v0oI/uSPn6GvXjPO1gup0tJGxnsF7dby1X/U/RakjU+NWvdsYvBn7IybNW1sgQJNdXrr1V9rm40bEpmqXtfL74zT3Cu9GjdVvmy9I3zBn/yJduypduxwSHt2vKlXN7ut30/o1SWrNOaBeR32iqp769eRVvbxMnbikb3/V57NsSzaLUpY21e2H7xwNz+9a+bupTN/My+bf6+dep/lFmqmXfsd9npI9VEX2a/TGGPfVu1y3igWBFJzXxUMdit0rYyrn08I/hRP+op9JhZH3kv7LBjzso2tEp1qtPylCj1wS1nsufPn9F7Lvh3ZR4Q1TNi2VsvXViTMPr0n5/67ece6aEMBp2fmlyeNjhTfSYm7qyGggYPsu7A4JHfsFWehE+vetdM1Dqr4shQNF1qsor2DVJvd4l6gMxcrOaietG3txn/xxk0M9Our1U9Z4wHfOttWWhDc5gf6ndDqx+6PLJt+1wOaNjKkGvfz07f+2XiYP+Um3T49eo3Pxv7dfYbeX2Op7tz7Jbtn62zwx9lRIKiJ40tVZ58H0fulRu3bHgs2Dr7UGsLEvmMsuNJk92T+z5PKze79bYmNqeSWyv5az8E9sS/g9gLR+RPn6c5GS0Vr94nBiTdbD+qA3th3QGH73KrL6I4geszwEUuX6h3e3lOfNFoAqP0Akrc6DxDo5gL+91w3LyrFQwABBBBAAAEEEECgBwmEa/S22yQ6v0zF7cQInLQubuq24tk2Xkykgr1RG1e+bi2tpdVVuxX80fdTj6tjwZ9nH17mjRcQGH+zVfa2EyxKInbGFmpqatQJJwd9n4D6hhq8H8jhI7ZsmCX/Skgza+v0bYiUK7Kr3EaFjltrcFsnWuUcULCTP6jrKtZ6PaWm3RTrrXB8t55d+VL0p721TH7gjslJJZc2L/lBpNI5f+Js3dLFSpQm3zn27RtQqC6WSseOFmqwiowLks4/YlTnGSncFPdzSmjLEwJqXqmDmjClUNs2OPvfrcraRhV0UCHnbfoZPWjbC8P6eFkPGS9aFilX+2nf6t5Zo9X/sVfhY2Z2JHWqsrSnZ5VDrz3/a+1ttuDPfqvsSbti2wVOwG3uHf6gSdt15KS/8tLvFFrvhI4DHfG9NGpvpRvIKtb06eXKjy9s/5H16vjdZndba+Vfbukdp7stuUco/6Zvq/ji5yztTbRSN7zzdVUev0IlsZ4TYauMjwRfY0eZtuDHmu5V6I3QddZrJ//FB/VqbGyLXa9sUujK2ZYwzyb7vFjnpCKMTU6Knfi4TXZsS7s24fPr9bCbGknv6a0PG3Wdt393S9/fhj96ZfXNzfjhrl8v897/GW90hlZssNenOwZIu7s8zWvW7r7TLMy4bLZ9199n1lJ/1jxNSFOG6GxLqbbkbXto71/rsTb19v/L0r/FqppsPLXNsdeZs+7Ym76ruRPj3z/5F96mz49co8UvxiqA3/+D9jSVed9lp/VatuOFP/mjXnxpkxptPJTauvjr2inLmZjO+Xdz3Va9HLtvyC+3FIyRmna7F3j+udh7/lLd85MFbT5rvGBH/mTdckt5QuV7pg7n+l6gYc8ftU8F7TduSVX41pDesiD2q9aQ4IbvPayJ/oCCb33/uIlhG6Mu5WTz/d8rTg9QhfZqh73OnSkhXdmn71l6vWhPwbGzFmja6L42xFQWBA/sXvRf3fefndPMv0/qERo50wz+C4zQ9Lm3Jay4cd8P9IZFVJxGBDekuwerq/A+YwMTyxJfuxa0dd+1Y9tr5GRHHTXVvicv3Ku8z0UDZiXTrlFt1SELNLd/DZoOW6An0muur4rHX6EhsUYeTXbf6vZQSjgpniCQpQIEgLL0wlFsBBBAAAEEEEDg3AuErfL7gOqPOZX5dvR+/TVkWJ4NBt7Ojyvnl3Nv5z+77YylzmiwAcUPh2KhgP5B5duA4mcgC0tqjlZL43AwpGPHQpbCzEphZc61nOzBPOs1knwn3GrltMGrw9YS1pvcwEbsFLz5Z+BB+JO9XlCmwFpNRltMptpxyMZOiKUf6jdZX7sqWnnW9P56r5I3OOkmr8LMv4cGCxw9syypR4T9uHVaUqY/nn8PVtexzdJEbUuc5z6LtJB1n6T7e2Srnng4VrkXWSc69ke04irdRr75rTV6LVbRrZHWcjzW+6fyd7Hgj606aco43wbuw1gwwn3axb/pz/GAtQp+sMO9tvFrZ8yJ/BI7j0gAyFps/7nBWhq3897q8MidX2FM+Xe16GobH80CXbWb4+OtTL3lu1aZFbRAn42N1adOqx9eGqmQD+S4rfw7fyx3i2O176l2v1vF487N8K/1SNlVVeNVEGW4VYarWQq2xfHA6fRvLWibmqq9PVklpNfrKX90NLjS3vq+ZU37zMR7Pk5zvOCPN1MFV92kqRt/GvsMsPF6KhtUEuvFs2/nH+MrXnaTL/jjzg5YisG/18aHn4vaNb+tPQ2z5Qwl0nSwKh68HHyN5kxt2zMx93MzdMukCr24LXrdquwaaIwboHKP4f4N6bVnVsUqUZ3XS5qKVnf1pL9Oy/SVO6PHcT6yfZ/OSWuehaeh3Xpmday3SKRnS/pjnO41S7/nNEs6UTZnD6f1PktTBHf2jpU/s7FDote1YMZCzfSNlSIbfyT+VTvOxjqLB3/c7Z2xrCb12xpPy+YusL+n81p2dhM+XqOq/U5Q/WxM5/q7OazNq9fETuRSzSmPVnSHEu4FLAXn2ThV22eb7zLfcdJ/T/pW6tS9gKUOXbNK6yx4sFqlNkbabW3HSPPt2v+w6cMNkeCPM+8N6102MU061VHTFuqeMktlaveETX96SUtecntJOfeK9tqxIM/YGfM0feJoS2FrjT0s+BO8MGC9m9+JfQ5ZjxdLg5vvNt6xe8gCbYh8duePtvSoKXtHR0satoY1IWu40HayRLMtx7zPubrKvWr4K/tgTrludOtAv6A1Kom/y9rus/0521c+5euhV6jCC8/UfUejdx5qSfO5b/d3Kx97yVtv2qSk7xG7x3DPLNDPfZT+fApiwR9njVFXzdItV6Vf11uyf4PuX7LenuZr+tdvatuD2VuRBwhkt0DH76DsPj9KjwACCCCAAAIIIHDaAo2q3LhWK9dXeD/S/LsM5JfqqzfdpIlJTeWa9lkr8aeigYuCGQs0d3Rt22BEbEdjneWdyFXuP37Kx1b5un3dS3p5c/wHffJ6xVOtJ8ysWGoya+n/RIqBrjc/axWssQ2nWiv6me21ck8+QAfPG/a7aZRsnJni9FU2ezb+2gv0aFyh6i3t2XYLou3Y4AZVCjVnRmni0Www3rdefs6rBPEvDG9fpYe3V2juPX+vsWfsh77/CB09DkZS0nS0lru88rVfe5UT0786To1OENJada90eylYSrjplwUt/ddu1Vl9ivsDp6+FufY5rXVtaqi1HjV/tsClr74lbOPx5BWPU0Gs10R0zXPw/3A7/zSHybVAgfNKsDov1X5gvWKmJaZJSbPZmZvtBEdjkcETzhgekalQYz8XDdJGFpmhG64paed1m2mhCibMUHG1vZYtOOtNoQOqyqS1vqWnmj6p1AaatrGVYhsHdKDDMTC847TzYJel/XLH3ykoX+gFHtvZJHFRU4O1Xo9OwWKrQExc2u6zuqr4Z0NwUlKLaG/LoMZOLNTmWMCwrvaQFAkA2ZhYXs8jC46WJX02uNsPGG29NBTrWXjCxn5qtABQfzVUuWnr7HNpSjupGz/1JeSJvc/cXfv/7nnjOS+d3LQFdyu44VG96vZ89K+Y6rF9Lr/otkzPn6G7buqrJ556PdWaZ2Ge9Wr5ZSxAZqm25i+cpY2PpU8Fd3rXrLPF71zZnL2f1vusneI1vPOSVu+MVe7aNZo/LRqUiG/ir/h136Xxpd4j5w0S61XhzbP0b6fzWnb2k1tgqTWLP7DPVPfY9ulrwbMq50P2NKdz/d0cev91L41i8axrlffpAe2rr9XG39jnZ2Sye4FZpWr65AM7v+hYbpHZdsq1+2Kf2ket98r7H2hg4peh+uYVa8w5bnBg4ZT09wLWK2WHe43se7Ftb9PYKaf4k3tJmUoUHV8xtH299nylNGUDmcCAPOU73//2OfOsG/wZbGWyXr/Rsayc8ehWqHLXZN069ysaY8EfJyXhtv9w7ystJaalCBvr9mhujRfGbi/anfZZ+rl06VYTNtz/uh55uP3PvLapWBP20O6T2ree83rfRFe0gjtld07VGljss57Io0a1Ddq2u1NvYTx1rlI1FrEevC8/8i/aFVvf6Z0+LWk8uX3/YeljI8uDKhjivoe9A2T2wM6jLhSwcYVSb9/kNP6KTKl6MGd2CNZCIBsEnLc1EwIIIIAAAggggAACqQUskLDuicXa7P4QT7FWuG63Xn7qp6qc/W3Nvapta3Fnk9r1y/RIim3dWbts+bOBb+v2FK3N3XUy/9tgA5gv9iod021XtXmV/p/GgO6/aVy6VRLmuz8RE2aexpOmkFuNPkIl6QIxVjGxen28MljbX9Ly7YkHLZ71dV/lRlj73lmvlastpZNvteLy26znUFD//uK/2NgfzoIPtPKJBzVx9kLdcFVyhZ1vQ3sYuGy27vmbUjkVGpEfD8f36kUb98N5SZTMXqC/+cJQS3Hmr+SztCeBRv37M3asI86+rPL0e7dpiO3ASQIXuGBoxpU5ddufs14G8VRYbzy1WG84u/RNM+eUW+W6VYg+axXNvvn+h+H3N+jF9/1zoo8zGaC5xIz+5gtBNYWt7FaJ6LQuXrI6WuE281vf1YSBFmqyZd7ktHj3GckqRe+7s8wqlAzQ1gvmjUgbAJIFNMYOtkp5x816rjmvuc/mB1uDdr0XewXlj5ObzSlyjo3Wo8472dN/kDuyTLff5R/zw/bptcjtaP/9NfH62zQxYbWwVdTfH0k7kzC7E0+a/mwB71ivE1mvu7npUte0s8+mg3u99+DnrSV43T4LQlZW6fBxe60E+lvPx9Ea8/lLrfV42yvs74X4+XbS3uQ7Yxi5AaDqWnu9XBp5vUTS8EXKFtSoC93m6cmF7W/pbkZo44bo+6u2psF68fRPqJAtKLSW52mmoI2NpZ3RnhW+UFDC2uH9NkZZrHyB8daSfsxQbXQ7MCSsmeqJja2y3O2B1Vdz7rAUekfXp1rxrMyr3fwrL+3m2Dk2EHh+g15t50ine83a2XWbRZ0tm7OD03uftSlCdIal4XzR7SFllflz7Rq1CXT2tt4Jtna0ErdCb/15RmIPIVvWtMd6/0S+K5zd5ieMs3U6r2Vnb85n6nV3fDvy0Puvda+W3L80ViZvbucenOvv5rqt0TR5/ayYFiirWrtMD69NLHLxLOsJbB8ne956Xiu3+b6T/Ks12/3ai24AI74gEkSw9I7tTefyXqDJGm1EXzMWvLQgdpvXVXsF7V2oaTMKVbne+Xyq0cYdBzQm1nO6zWYhC/783A3sjtAt37heW56IPbeeNWq27+H9W7X8F1tVYCnG7iwP6y3f/fCu/67RdZ+LuXnpcUdYsCh1sME9vn+sSXdeV/92JjjmP0bDzjXpU3NaAM4Jzmy392Xgsq/ovluu7rAXa912S+f6ToOCOdGjBGT3ETErZ9mLtfHvosAAa7RjDZHilON019edlKzOuGaL9fKf7LoPss8NryGIpR+1e6P41Khda1/SxtomO178O9QZ13Hs7L/XVbFgZpOlQ336qTWR40y65fu6zhoLMSFwvgrE3ynnqwDnjQACCCCAAAIIIJBGwH6I/Sox+BOwytqvlpdF8mLXVr+jdWu3epWcu9b8i97I9481kWK3/azF/nUzVDIyT+GGSr36oo1hE1utau161f3VbR0OJp5irwmzajeu8FUmWSt2yz0+5S9KlWc/So/VVWnj+tdVFatsCm/foD2zx2mMDVo7cepkHbNc4Q07/2A/WmOVJ/3GafoUC3AErELhDPb+cVqRVlVGK08jhY+kx0s4jegTq7yaYBU+m72W0U5lmqUOcVftV6a/mWqtMyODs2/Vqy9t8P2gdlYq1Fe/dZuuivXOus4GMP68tch/cUM0qLR9zVL9qeom3TO3LG0FS15BofKCvorgoA0wHTv+KKswCtrvaedf4pTn5VGXBRAK8txBgRPX6uhZnrX8dcbDSTc5LUanRlqMBjS2/FIbP+cDS93ipiWz3P2em1ViePOje3Ny/uf2Sbfn+PwCq+h2xixyTzF8zK3YKVSxtYx15rvLvK3yGj2j/PGXWppEWyPTnkaxyhMd2Rvp0TTKxfZ2fg4eNHzgjXFQcGVi5VtTvRvYGKHiAtfizJYp3iK3K/tNHLOh03totdR+z27yNpt2+wz5Xv3e/I4eNOyN96TZvtoq0tJskD/xJt15k//9l9jrIXhB+qrPgDO6uztZas5IwLCpRpWxzzdZwNJtnO6u5v/r3z50NNrjK98+g3/wF7HPP+sRlno6oI3/Hv/8ShmkMseVS9yW66W68+vjbFe+lECpd+zNrXvr177eDt/SBHsLNR32Fp/dB59s1RIbTDwy2fhic650Dn7Ae0+3PfhpXrO2O0w/p9NlS7+r03ufydoj/Mr7vskvn2c9IVIcq/cITbXvqMrYmFabn/2fCs/+hqaML1TQvvf2vf+mXrAGC+6Ub9/D0THubM5pvpbdfbb5a+monPfKaU3n+rs5z1KKWYFrfd9pieUv03WxBjTDv3C1Atus97X7nZdr5+t9JrT9LpR9F0Z6wiTusM2zc3kvULvzPe/4XyzpfA+UUX85Q8H10R58VevsPvWq2Bhn3l7N8p21etpee+5rYdqCb6kkv07rYutMv2ORpgbe0ZInXoq8zp3EbLU74ve8zmqhd95T6Hq7D7LHTYfj49D1TXdPF9u392dwme78pgVO/Y1IvIXpHtg9TtN7p9Ubss567j3hBW+TjuN8rTTsjQR/nCVh63m2+MEDumvRTSpI/3WkY59Y6lIbS8e9p0/Y6xH/95ITVLpGE6wNQV0kAnSpb3w9S4Fn97NOkNNLoWo7mnqLHdtvap8Nr202e1uWfLwCf49U28aaNkSmbS/+T436zj9HjhubxR8EzisB3x3jeXXenCwCCCCAAAIIIIBARwKfOAOzxldKHIjc2umOHK0JX7xSKxcv1a5YpcTG1X/Q1EUzUgcTRpZr0V32o9zdpY39c9ePCrTEUq9Ff8DVqO5T26+3grtiZ/5aq8B34hWTk+Yu0nXj49W3+SMLNebKEq188NFYme2nv/PrP9cqqWbNjhyoNmADwkZajkrT75hnKSk6c/zM1/VaNg8ubedHdX9N+z8Xamw4V0PyhkbyvFe+8qjXK2b67bOtcvqAnv2nR700aW4JSqxCbs70cUnXIqCS6Qt038j1eiQ2WHFo50t6eEmT7r/LKo3cjf1/2+Ruj1UO2zpvrH5OTcUWzPOvH3kcVtVRd2bbpe6Sjv4GCsZp6vgaBUZdaj0ZrALsQum1n9vrLbLhCN16g9NiNDqNsfN6YLr7zPlrvYIWPxjpTZNvLXfvmXWpf2HGj5NTucTPpsbSU71kPXZSqJ2o8ypG0+a+T1kC65VhlV0bLc2dk2gtcuwUu0+56RmcWfen97xrOuGyxMq32p27Y0dKbKl/Bg9/2rs6HbLaTb+OjG8UKYRvzKnOFqrhk3jPtfa2rbNefQ9/EtIP7krRe8I2jL/e2u4lUDA6WincdlFGcyI9iKyFfMJk46DltjueRFg7LD2e+5nvBJmvKGr7ob3r18s8x2kLvu5V3mV0bRr+GG+Zbike58YqthPKedaeWFqi/8/tpmS9F/9+cvRzMaOCRwt19q7Z6ZftTLGFrZfey++73wU2Hk2b1G/xI42Z9V3NDf/UeqU41bUntG3NMvsXX+4+Cky6rcuf0ylfy+6Ok//atezE5UzeOvb8HH83WyOUqVPHqSrnEhWPLFDByKHa95r1lNgZLc7UBbO8sX+CNkbX/T+ZkVDuPWsftZRj9plk76f/+ztp7tEStkjx5JzdCxzQtljAUNbI5fMJXVBTlCvVrAGlmnlZX612XqPNW7V934zENJ5hSy/pD/7c/v3oWGm+NG5Ndr5Oo6d7flSodS/bWELleXrjJ+73X+ygvvHTGva6y/ISerGlKp43zxof5TtjUnozMnwQ7vpn/77Nz+nptW5ZbZyjOTbG36evxIPezgdY/mQ98L2glvxiRfT+vLnC7tVrdMs/fFslbT/uI4UeWFCmseNDynUbsdi7rOlwlY3TF/suLC7TpCH2CWABmtHXzNDEQSXS+r2aeoP17vQFdybc9F0FrUfqCStHU1NY+ZdaI6LkYwaCmnBZqRrcRhDhBlW+X9Pm+zJ35GTrvXQg2nvOPntWP7ZCBT+Z571XMtRmNQR6hMDpf+/1CAZOAgEEEEAAAQQQQCBZYMcGX34RJ7d/ioHIlTtacxfO1v2PxWpzjmywFGNXpxi83tLD3OIL/rgHy71UX540Qssjab4s37g/n7q7Tpf/Wjqty+LBn/huhkZSS7ipKeLzo4/8Ff5NLU6r+HSt4JO37Nxz70bcl74i1R5yLVXUqNiC8D5LZRZLiRaYaMGpSPeQEZq7oFwPL4uOt1QydbZmTrNW1ANS7S06L++yGVbhHNQTS9ZEWlBO//IVmVeI2Y9yr4LT0v+1lx4wcrSj3trpC5RuSWCEZs6d5y11UsJFgz+Wfs4GsR/Tbq2JpWJzKyJS5Z/39prqgduLKMUy3wsk9H5F2rRz7pZWf9GpKdDB66FTO+vSyhZEfdutHBpn6Qm9V6rtzSrm3JR8Vvnir7Tp0qG620bhvXo5Fvx1ijZ9dqzyv9PlbNS+D7x+erZ1oWbe/nV9sdjGUrLAZN3+Kr2xcoV2ua3y96/Xy+9cqbmRMXwshaJT2RXLjePWb2VcBEsv59s8480yWtFSgm7+1aNa51X8O2kg/Skoo3sJvb/GS6EXnHhbtGI1owM4K4X02jOrYp8xTlqxLlZWZ3y8xBUrX4mP/TTpFkv91u5njLvtaV4zdzcd/O1a2TrYaZcWW3q+Neu9LQtmzFKB/2PCWxJ7YK+bY/4xo5KXx56HP6pSbZOvQcTZfC2nKUNnZp/b7+aAJsyapwluAesq9EQs+KPLbupwfML494r1sHD3cbp/z9K9QHjfe973fPDKK7rUA9M5tZJpV0uW/tWZNm6u1LS58QYjClyqW6xRyJK1jZpzzwJNiKRss89t65kdtF7XTcPHaZSld41M1ttr5txChfet9co17ZYFCttnxeYjJ7T9Twc0wVLM7fsgFky3ntF5voBGdCdp/vf3VkmzSurZbvA19dLUcxu145WntNr9DreVimcttO8dK/sb0R6gCdvlWVq2f/q2Xv6p9WBtdpZYo5efP6q53/muxlrvneQp/8oZtq+kuV4610LdeftNGuV3aR2q4mF7tWvD+vg9pbO5fenlWipdd2qwcZx2HLd0rzOujqejde4N7fPZm9pJ6xi0NMa3TKqK3Tu/pyUvvZdx6mdv/zxAoAcInLHP/h5gwSkggAACCCCAAAIIeAJWSflJ/AfmpOsmp2+hmF+mGy5b67UGPmY5uNtMluaiOLkFX2ylgL+1eZ82W3ZyRn9Nv+dhRTqC+H9oOnsJNyrU0KA/Vaw9rfFBOlmgM7e6k1LpKbfSzVIq3eCkVIpOuWNmaNF3rlDffKeCObPJaRm56B8sT/7RfEvJlxTksgq7fbEK6DZ7i6TLu8bS5dmSlvZ+TkRrhwomXptxmdocyz8j9F58oGYLSM6Z6PZMsdeqFTZsPRf8U8DGIWqIVa7UWa+V2gkm05L42gxba98hluIueRiWsI3fkpxWxN13YGCxpk5y6igCiZUW7greX+dYuZowrWs9j7zdnOsHNn7RW/ujB42kY/IdP7w/XjE3ycZl6GlT7eb18etur7FJSQNSZ3y+1hq5MccZs6BBDTlf1F13z45XXFlAOX/UOM1d9H29seSn2vj/s/c+4FFV577/99AZCZE5YTQTnwlNkCSGGCDhxhwC5U9OzKWACI2Imp/iCUqRYkVoLcfCFVqRB46lWhELUkThCDxRETn8EWl+MeVfSbyYCwEiRBIkHDIPmdSYDiaDM+Xed//fM7NnMpOECOHdPGH2Xnuttdf67D+zZ33X+74y6+ptH8OZQTOTe9FzVxxsk46m0xvDPLzvNR5moXazuShOy8YNUiwFJbNtVBGm+8fWoNgomzeXy1kGY0ZBZNfJOXJRqcSDSZ0609itmNKALv50nyuhQUJJuDPRoHr48SI6e87a70jH29Z+3ZHmEGKilMrXrSBuTghpoeUKjCPYOw7p5FrSRs/o6qMUB0S53i8eJCsDcjX12yJZULo213Kk/W03f3d+N4uNacaON7fLzSIrtWlaDLXmSw24TNj034aCiOxokQVp5ynUXRxMIrEf26temPrFB8Yk+x7eBar2/0VFPjxrgLoe6UpUQjayepeJ4oX35EGc82aKMZKUeoSYPktHKVv0eelzrCe3xMIyfhq5NPQTOUwJEzEt7SBZFY3AqLRkOM7F4TBZKtXtPwX3cIBCvIlLUkaSD38pNcj/yiSVILu7LFlwmbaO4jLq3uuE+IbT24kBCbI8K/hf89Fn9WvyZB96D131GqaRCCS4cPNf3Of20mSkg5RM1yWJRwl0XUkLxYAU3sf0L6jffY19JSXiJCQ5U4gPC1LzdAKQf86r9AwOsaRSrMCcYxQblJ413sot2JexMCAWWYjivIsJ9AgC+u+FHtEh7gQTYAJMgAkwASbABJhAVxBww6268CJLmh/6CQQ+hzDRgCb9EiT3C8JSfa4xYODMRLFS9L/7fIp39YYs/DRfrMW5r+pQc+YMLlxsgEsZZOrq43WwPuVnMciKxuEe185Mcwo8vPWPqkul8XNpgIz66XU3o67qFAVvH41U1GP1yg0AxTEKb6EZn7ePwazpiYHZyfJJ8Zuu7lRdowju8iaqyeGseGkUWx9vJJwyvnmasefNLfJAAQ0szBbcZbXBcbYe7t5u7F73nmIw4VtM2XKWYe0qaSawkqR8WmgQaIGfezivaPml5JA/hRPWi/pxO7mWmRzBoDZxEwbxw7PkoFgiJ+VZxH6H765N5/85pA7INH91Chdayf2ebE1Wc/AvcjPomZASRNHtroZ2+XEork2JHPeF6s4ar4/LE+HBhEEzGiALvViQX/gIDr+iWLs4cZkGyGz0oHSHPSvcbHAIj3r+DHb6JIU1GOBtxOFtW7HvpOzGR64hZ+ozmJQV71Of4Hbx8DuKS08zxs99xM9KzK+9fg0Q3Iq9UyZf/xR7p1AVeaXD+GSP2KrPr6n+m2T9VSxbUQIksD+qDapLWXVtNxi07dw582+M33Yn2+ZXW6c3q9XnAMU/oxh7A31OjG/1zcd2+liJpo6biUISxZUi+ROn4tzhLXhnL32PicsZbNtfi7mixXEXX8u+TeuirW7+bqZW12x/U7bIAHJnzhTfHVz0vuOg58a5bYJVSqiukRXH2teMM/TOw6LFfhZ33f0uQO4fdysWhuT+LaOjIrzYQyty7k1GpRjPqwEVJ5vJBbBmFe48WYbSmmZYzNLV6KxThGtg367tuEyu59T3NFrzRA/G/dOX4hekpQnvswlZ9Iw4TIJRSzkJQW7VDe9dKcrkFGPMPqlOiukovB/ojuSz33CD2ttGgorhvsBEJ1lBbdzsK7TkFM4n98hhtpPieI2ftwBYpcQEpTh5JAJFPT8/0B2c+qLTB1H+k7D8m0bd6EtpgjRpsSXDfrvyVNBl/I5cu4ku5MJ9r9WV9Vm1YhJ5K6iQvRUcPnAG+SnZ6nPIJytvMIEeSsDgDuuhPeVuMQEmwASYABNgAkyACYRPgGbTyfNFqUwDmmlyXUhXOLpfokZCj9Um/MzrpsVFM9DfpPgTIQdBqC296e97E4X0sV508IIgOlf6pjYoQr7ZLTRQurm4HDU0eCAsllGJ+EVWM5ziLF/tzAWpTkt2HiTxaXTguaVfCcoPhdQ0GuQN4V5Dqyz0mintESyZrnPBEjq7bq8XlcVvqlYBiIlHXckGvFtRKw6AWNL84xzpioaxanS9asXikJrQh1y/lGDpOmMBScsbei2d/Nr7D2iHLBEyNlTIkp3aaU3/V6R+WosaYaYszfRfv/xzjCr8Oc2WJddcJ6VZtkIA51CDvp1qwPdU2H3huOreB0hGTrBAB13ZPmsqRpF2vl+clU3WQnTrhnzO+h3be4kGff3Swh4VpHLOunr/0j7bzWcP4a2NH+u+C2g3WXw+8eRUDDTQ/4R4RvsuKlXcg6ReX8NxUXu+6a3yBLHI+VUD+vah/dE2spZyYdvaEqUwcmhmejMJ92ppGlh0O7Rnm9vZAMclGjYlK0Rbf5pg0N5go1qz8YoQ10iewA/k0MDg3xppQF05Oj0NvyPLCqXodxTo/FIjomh/lC0e1tAPEaWU+Gl4znxyBG50V9sCj2yQQrFTDsvPAWHvj3JCWTiSyymyjlAWC7ktnW5gETlw1GN44u9yjBrK7Kysg5sEoCgFv1JBiM/2ruUQRTu1q7u/m51kvbC5UrkPaAC/bi9WbyArKqEX9J2RaiBOht3BfzbI2c3vAtX7tqv3vH3siA67f1N6Ys+g9yUSgARi1fuPwT1Mi7V2+UI5qlWWSgn5s+4oDqsPBDmtdx9MyB8Eq/zsM92RiZzeH5NViQv79wpWL8Li7zZVSg3+P7ny++3R4Ls7s6e1EaXbN2D/aeV6ESqz4P458zHc3+q73eNYSQSaD+/K1+R3sUZyB7cBTywiAVLnblh5b4QtUXSDp93CjXhn5W/k90oPvLH3YdHsJPWoWVPJVajo0lhNkla64N1TrZHenWeNq8XnphEoGBXquaWW4BUm0KMIqPdnj+oVd4YJMAEmwASYABNgAkygcwR6mcU4ElIlNMClm/xsWHF7b5Vhz2g3rD38RK/go1wLPi4UtA8Zg3vSEmG73Yp+/azij/f9a1/SubAJv/quzKm5vmtEvbMNA0loCFxI/Nj8kuReTxGsnOXYpnh/kQvYbo+ieDdCzCMSQ6L19ZBP+8pTqnVMOg1sRqmeMtrI4uA29DU6dzSwqf1wF06+Rxv8DGxkWCnecw1wIzNCSzBd/5WjtBzF/gplg2bi2ulHfeFUGqDV0oQ1Uy8Xipe9JlpNWXIeo+DiNNgg5xH2baN91b5FtK0rWu/FurQ9HV67cJEGYfwsGgIq8zqhxqa6JYJR5YCKOp5gslJshMWLKVbABooVIFhjeHC4mNy/6KrMzU3VbfWM1fr/rQ3CmYZkixZ23dEz7fbzkJsmssqz6cVhkkm+9b0W9W3yqu51KPV2izS4RvEqBNdFkqjUhjbhfg9yKXnJLaayWP5Z/9wA/AOFCwOHuYU0SDeEBOEgy+VmnX8hlJPVXXmQnEKyC7s3viHuN+XMxJJxatgjMa2imNwViWvG/7lO7sTak8I+M5544cWIhLPAGkmMcqgPRqBiC1aHOjg9g95ZLV0vOTMWY1JKJ89ZYIN0KZ1vm66yTq+6zp7SiY4U9L0dCw391Ts8J7jl5MBcihFIlkDiULUiYnTRtdzpThtWoPtu6qbvZudnm7B65xlda8hqsUxnmUfvAJN+/jCm+X0XgnhWFwvvEVQ0ZgTmziXWikUv7auhfduEff7lhCN157sAvdsUq+IiWUj+S/BnjdC0sBZLEn7Unyx6LlJuqr/Olae6leybMALpNHHGSo++w4e1539SFsVQFF57aDHR29NhmmwiLrHyM1baov8tGD5+ECp058SSQ2XV/cFXvCQid9Vi8nnnk2sloXb18g3qu5+YSm5/5zwxBfYg3wfttycOk56bD9cS5d2pFu+W1mLJZE1M8Rdite83qv0KCT/KQZq07x4hyesVnr8+uaWc3+mey1JKp/5PyH1MjanZqYq4MBO4AQkY3GE3YC+4yUyACTABJsAEmAATYAJdTCAKUcJsUHE8rwHVX7kwMM1gyrd4VC8ctdrAX9JdYbqV6OIWC9W5HadUN2lAHAWrfYYGQ/1feemHp9FAxzVoT6gqbUnCYFiDmOUczWzPNRSATLCScAXQII+PtZIZSUPuQfqQwbgrhQIOiz/oqb/TBwcccv+FRWLMI1veMyjMD29AxU0xcJQzGmWiH+C9BuCn5ALE23IMqzeWqMcomL0Ad/Ul7uKPdzUZUVF94KjchM0lUv+EPfkz/jXYOLRW0GCtzRU4AGAidyE52eQeJj0VdmH0Rlj8TzMlCZ4Ja6gjUSa6nimguJbHDHt/GhQTBoUMFud5ZZBNHvBJoBhLz2XDUbWd+qS4CRuMWc9NQV8aRFcHNcS6KCA8qWyVm1/TiYzJmDZOGyQxOKSU9DeyapB32oYkdohX0Loj2tEHGZPJf/9dJVi9ucyvf2OQY3itRnSA6yxzIyp1M8HvGabNTO5IQy+UvoH1ZcId5EFO4QJytaO5HfKtzyu6fZPSLBgoBiKny/QW7WKuoqDkBUOMB86dNcp1SrqR3aZe3lppF2pIeEw3fHb7uhu0x2ttFMSV9Xu1uk1JeZjzb+N0cYx8e6FuGblOVHe2s0L3p9budvJ2+W66Z+k5hvasRkMct7PnLHjVnW9b8Loj33Pu2OdqIUvO4IgsNPqo7qHUKrQVEryFrzH/YXHtmujYtawdoKvXuv+72evx/aYReyTEU6LvwpyMwRjYX76HNWhqp20U605000suYi30/azXAGwJ8j41t7bSfe8CbSjdulM9sClrClJ1liXqjohX+iB95GDs2yZYornw2ReNSJfjltmG5KFwCCWTlUkdCUDidy9ZKs+Yqlkqu8/uVQWgURPuCXhG2bLzYCMBSHlfyh0Rxvc8HdI+oghzskwwkX9Yt07IN/UiMUQR58S+Uh5KE06pPh8o5mEUJXq9kgWkmFX/n2kAcodYsO2kdEdlTZmJguHhtU1fTcA6uYMrJKuftSQuOchF3xx/97nKpAKy0BQkHu1XgwWjptyHBBIcyZkevCZ6v7nqDqg+IMHgWg7I08mEbjhEJ1vIxZlA1xDga71rOHItTIAJMAEmwASYABPoYQT6wH4H/XSTXYxV7CtHfpqfb3ilx5fKsbtOG6Tv10cekFf2d+Nnc329ejRb3lQD8Yd2hwpqrJa+9itRtiSaKVomDhzUnSRfI/7B1OUmJJALE/tXZ5CYmoiE/ol0XihQsyJ6tNtM3SzLCAZnNY5xSCSLBGGx2mhwyZaHudOasXqbNFt2x8adeGJ+EQZqY8diXiGmg178yZ2xMIjAJWYP8Z8J6XljUPXXr5E6aBCSEqn/NnL3pB+9ClE6+K6v0dwk76UBCd/Fi/oaeVZ1DFmOyb+YLFYrLLkUv+JvK1AsigWn8M5H8fjFk3l+g6Ausph5Uyf+UMyi56U4Db7HCdzShCcgNSk8sS6wlq5LsdI9v2SuBUtWawNzwEGUHsvEpGHht89g2LLrGtkVNV2q01mD0YDqndqwVUeq70f3qSLuVhw8g/FDRgQMHAr1us8d0lwbog/6yLPObakk+MixcLyVJbgwZRACveNQbB4lXg7VlZBwm1AlLWSNQlY6pfK+yrLjuD9tdODxm0/JVkJCGQsSYuXnNllRfkjuJZXFNqqIrOeMBSglj/IpDGo+kSbE+1JSfD9Nvdqwf+smVItCiwXjpz+GxFs9MJGlB0gAmkUxJS40uai8DMKnOLl7c1CMoJ3CQC61eMh9KByTKLqAswfcwz4Fw9gwYdSshUi8QPd9kGPjSh2KFXd4MYMxvTAPfWiwM8oucevcOQvVxM63LVTtEe272oAq1UIDyCCRur3FpDs31ecbMfwO4wkizqpydRBdsDqRlk5ey+01rpP7u/u72Z4+AqlV9F1Mz4eBA+i7MN4Gq5H1R6h+fRe406m4VtQJz0qu7noXcBwmV2WKikLPo2njBitN6PSnNU2wypEsoev2fw7X8Ik6YYImguzYok68mPYTTfyB6xRNeDkoHT9mDHIHGrzbul0+8RJdl+mdS35nCt5wipX2n+tQfce/omBSHhJ0XzeC+CMIPupC4lAziSnfeGmCQILuReuqC5V73qd3byvmzJ3qF2tNKG1CxsMz4bbVIWEkvUN2iZgmtyo6WXxWt/WJg8XnWU+Twc43yJnOkOu5NhRYlQwWEiozkaDvm1uZTEOtNXzuCt0wYC4foas+mp3y+15XVcj1MIHrlIByN16nzeNmMQEmwASYABNgAkyACXxfBNLz8wCaCS4uzjKs3xWPOZMH+w4ktlK8ndUfa020jcM97biE0TK3s3aV3JvsKKEf133EoOjpeVOQcUf4r6/O8/XkcszfioIG57eu01kJ0exK3QCVf4u8PlY3/ns7ua13b0P+5s95RxjGVTEJbjvmjDA8mItigBzY/p9wZszGjFHhD8gbVqYmtqGmSv5hHpOEWD+xxTZsKuZebcPq7TQQe+UM3nl5EXKmUUB4QRCg+Et73t6ACnUgx4z8mb82HjhRjtfOebamTcScNCVz4GfzhVpc7huPhLBFMaqDBm0uyOc2QGihAfAq2TLIQq4D/bqP9KkLUPDdSuygmbXeuhKsfOEUps2ZScGq+6D5HLmw2bBTHUwSgsk/sYgEsrAGX9pQ/blidUGxh+SB5cAedzKl+Qx27CMBj2aCu78jNzvTQll20EDVnr0BB6zY9gbqLjxCz4NM3+cBbQXeTzTbPMQ9FlD595DgdukGgMKOveRFzf6PUfk3igND4qqFxLL8YdLgdh+rIsZQZy7uRPHheEwf5TdYTs/O4g3y4CJlM6XlqW7MovoPRhKJw3UiiwYUk+ixYKrvgOi50vd9RKvMFG1w0D5kmCog4eLH2H0ylayI9APvLpQWa7E2EDMMSfIgpJMEJ+m4gsjySNjij9DUKGt8gBgsdkH9zwtBrKkWt/sgPoVEbd0j3WSJo9hC+naqBcUVL1l1ggZyhSWKYkwIgrj/0kwBz/cda6ZrjgZi+wzB/RP9r1H/EvK2iUQwo8BGSnYaiO1L65KLsjgkkdWEruno7DkT5sUHu57QybYpXej0Jz03RSsJsaJwnlGSa7zSi9L9VbdzAz7rHxh/xOs8jmLh+0ReLGlJ6nO3M9eyUt+1+uz272YrCY9zfJ8D+r65m+vhuOwnFOgzGK6Ti8ELkpWIjSaZ+H7fdd+7gD1jIsWeI/e99L1sH/eY6qbNsMmRJkYlY1QSsEN4sJHPW0FkgSJEXDokT+iQKt325htw5E9Elu0y9qx7T7VIy3/UyIKZBO31W3wsZPdveB/pS4tCuhB1n/0LiV00acpZgh1kzTw3PxFuiuu4XIyBZsa0515Ehvw4dxx8A2tLpPtn/NylGCW/AzsObsCOCil99dZELJ2eHUiFrHWG5wd/ngYWCD9FeFbrdCupIL3L1ShfHpRSuXkDbFOUZzS5FRbER/0FpnuAVpfsJLFKl6A05ds63TNHSTT4bHOrbopDuU2F6wzWvrwJzhgLrPQOJMaikye6GdTKSUygRxEwuMN6VP+4M0yACTABJsAEmAATYAIdJUABU6cN2UsuJCTrHifFRVh6ehDGjxuB+BjyjF5Xjk/KTvn8+M2fNsLn911HDy2WIx/pVbr4NdacicjwGXILrN1KFiKALF7UfYzfrW3E/WMyYb0VaK4/hcMlulnGYvEGHD54FLnkPkV1JabrUQW58Wom90vCwF9ShAJUYOv8U8jN1phB2L9dGPRvQMXJZgwcpg3i+udWt70unDt5HPtp8LlOFlosP9RZ+qgZO7hCAojiGs1GLuYCfuRTtTYK5v2LaM09mCAIVGzzO15viiXzTBFS1RmgfvuVzUjPMw3EOh3nSaQqx1+PnoKLBozEGCI6P/RK1cE+L3z+F3W2ueBGRb+4HWfUAQfjGe4mZBUuRN/SDdhcJlxrDRS8nmIo9KZVaouyiG6zZpC4ogw0KTuCfbrqsF9xSZdEYqB+oCRYmQ6ku7+pQ+VJZbA1DjkFwQQgL6oo9s8+xbqPrB7GD6dYCiVSWWfFe1j6341YMGecrhVOlL6/Hf10go+ZJNwKpV+6nNfTqvO8NmplSvYfBA3WUg8ufEZBxFuk/RaKraEIQKY7RqMg7WMp5gbtrtn7JpZWZWMCPYts9CwKfHbSjHf9zPNe8cgfF4862YWiiwK/L700AhNy4tGXZvxf+OtOClCuOcuy0LNRf72Y7rgH+baPRdePQusq6Tw6ssZhwr+QJU9LPfZ/tBN1ums1Z/Jo+blNIuQR4XkkLa6TR7FnVy08moGnskv9dNOgXvjivF9FgqGH3/2nVmywotiFiLuCxIb45txxVJ+UBb2Y2zAhXAHI4Hg+SVd1bac+BzS9k+dMcBcY7HryaYfRRnttMyrTgTT3pXp1QBy9E9EvjGdUQu5U2MvelJ+pFPeJnpWfk+utXIoHZCEnUfUnD2GfEmNFbFMcWX9oFmcdv5Y70MHOFrnW380G7XOTdUhNzXFU/pXi27TQNdp7DBYtnhj2e5j7wufYLz/DTNFm3yN057uAhd4XFs6nWDwO3JOriAa+zenMVnrefXAMsmPSKD83aLZ78MRUD/aXkvAtcGihd8LtG3xi3tknPoNcmuDhv5yjd4BS+R1M20cCw8ZyLHnS2OpTkJBLP1KE/3jcL08MiIq+TXwUCu7Rqs41kgAkCTf2fyFrpZJN4n3318O1GDVVujfsZI086rMVOCy0+fR27DiWjIJw3h+1hnb9GrkOrtHX2rsB+3Y26FN81+khellOcdIkKKf2Neybr50t98VyrF67U302VRavgHXmYuOJR2aTeExvi4veAbXvUPEQNvodEMYzrZ3m8O5rQMBNk92qzjvJUiyCFwZ9O8jVopdipGUNS4zklUNfQ49Y7yC9HtF37gQTYAJMgAkwASbABJiAQMBnVM0XSUbhr9D89gqUKj/MWs6QL3VtgFCfO2fafN8fyfTC3amlA2+qUQnkC793GSrkwU3vxaPYUUzWDiGW6pLtqPm7VQ1ka08lFyBlJXIJijtAg6DCYs4mASoCCyS5gpAftvTRsJEAJIwhVO8/BvewPMOBG29rMy7UncHxY+WoPC0Pbqo1UyDioIHZdYMWt/gN7qjlfVfcX9WoAkh60HrJDQ1ZPMyZVk/u4EgE8RM/hPhLTywIz+1Zu7/GyM2S85ITjou1NLBLg7t1/v33bb/RljCw5SRrttXFn8PS2yOKRlK+QUj3i2fjOKaII/EhrXBS84swzbFECp4tVKYbUAdZwv07uYaLZCzhwmdarJ1ReTo3NEYd6kxaWD+g20TRQLBykpY4TP/ZY0glNTCjfxlWKnGgaNbyxsODMI1iSkiLB3Xy/SIndPojjCgBAceI9MnT1qoJqNY+4Z81vbWTf6mswmdQs+INVOueRbsNn0WCldz8gBnvCblFyD22QnWL5KVBrt3bA7pK9142ZkzSBsylHH2Q+2QRqmmms0Mu4iDLnnfoz3+xZD2CSUqMIHIr5PAZ0KxFRYV/icDtcMT5wFKdSwl2Xehj8USkLkXUHOMrrHPnjFqrE079r6fwm2fctvbKB+OpL+e5ot0niLKQbW4YC8X6eGL2ffjduo/VVw3HyTIU05/Rkj9zto+YKbg07NC1bFS5Lk2jpK3pdoe92p3fzaDJD66/NdJ3YT2+PH2Kvg9r/Yew2283WT14yP3j+le2wB1jhksQjeTlnnRf4aX73wXiMCo3TIuVXuG9zyh9ixo4GpMGKlu6z159MDCLrC/p78Jhit22lwQLv/cZx9EyVN4xDlkpWtucx7bjHdUF52D8YtljcBT/hq5r4lm3E2v32sh60k9sosM6K3eqbj9teTrh3pqIDDpuJX1f1ByrB7LkY0UPQn6SmayX6L2l8hAcBYNk6yJyoUnfyVUvbxGvgcptm5CZNt/v3tH1sxtWnefOSEfpPQj3j7di985yn6O2CZea/sFGllnTJo7B8UsemOl0evxiXJlJrJEWuu49FvTVPR+Vir2XjtJEr53qs0VJL93wEi4rlulKovAZNQDTpk1BA4n42hVEbkuj7RRTM9mnefpivP79EnBUkrvDw8r7aAfbQuJ4BgtAHYR3UxXz4txnB1HXRub95BJgFF00XbU0nz2KyvPN9CAi88yRmbCEO0OwqxrA9TABJsAEmAATYAI3PQFTtBToXhwGCfiBZaHBl6VIpIHD3XsOwikPZgo/y5RhE0tSNn4yidx3yAHMFaBRsYmi9Yj4yh5Qr5JL/2mGta/2k0zYo/z80+cyWtfyWTFpwXxEbdtK/sclocBEP6oVV24mG/0wnfowMqJOYfkqzQVSH60CmBLG4ImJzdj26XESCpTBEXIPdHtYw11GzQueRj+Ax2dZsFmIKUPuQKoujaEYCbrG0AzczTSILLhFCVjovXT8xDzkUBwG1+kSbC5upB/I/m1swwV5QFdw7bSjxepXTRu5AbuNgvNOREK0tKu6XPnRnox0/1mvghhzoZ4Gno7iswqyvlFqC2hfI95Ztgg2ujaysjKRmkxxiyz+bVMKBz/P7gslWL7OeKBQKG0hV1k/GjMCGWmBAy1a7cK1aoKFBruEWfaCxZCy5M4g3/nKhvhJcVVktyroPwwJ+sEK2i8O9tWTEEVCXMVJGigKttC5XP7CXygWywhkZVNw7h/GU+Bt3Xn1Lye4wVMGk3qPwI+MYg34l7lW295G7Fv/Gg5fVA5gxv1zZovij5BiSckT4wL9YfV2uMjqZQbNYL5cqhsYprveTjEQlOeDUEbwsa/fFtLCXe6yG9mghS6tupwjK4W+fucwsKQXl1u04W8tlk5gzlApJn+B1RSPwsWLya3XXvxXyVHtXtFVYidLiPsnjfOJAaHtJvd88xbDXrod21RLS+kaVvIk5UzFg5OzDa30YBmEOS/MJzea76PU8Fq1IGfqY5iUpf9tS7Nklcoj+AxxZQevRTg3YX0vGFeREM510Yn6jY8qpZrI/ZvxZdXJc6Y7aMD1pNsXajV420KVAsK5z9yt6hMfFnqmGzMIPE5UwmgsWZSE/Tu3B7kWyaqUXCAW/CTIvdChazmwHVqKWbxnhK9GS9gWf1ppca27v5u9tVj92w2q5apfa4CYeIz6UR6yMga1c15osJ1EbuGJpxd/kvJmYrjvlyG+j3eBgH7JCW4S6YV7wtSLnjY0ZuasPqWK29rTO1jpEOn0TnPhNFlBkQVQjeCWzU/8EUs6T2HHRvqjd67CGQ/Ddomsn+U4iIKMUDDvMcQdVlYAAEAASURBVDEOoPXhnyP95Guii0snxTRaiyLM0cdPaz2jc3VIFju5+vcWK1LvspB1Lt1jdRSv6Gq2aj1814/uobRyaopgBdEGu/J+YBmMGRQfbvXOM7SP3rmoTYau4EJ0v+t2kQtLxYVt7CAMHz4CCfStt1aO2Sa0r5jcBVts8bAnkPtOigVmvd0GWypZzv6PKJhJ0BPEe+27hL6Lrgqt80CcS0brbSR+uskySn2VophkxfQeonxnJeUV4cEMYPMqaeKDaJleEo+c4dm4q388rDH0xKJYQ7E0yctuMO7qcbnoaLQI1iLCcUkkguU22KK1Vgm7eel+AqZbhPdA4fvHDHt/vwdVO83xNjVIv13VC6edAj149z9dvXr1/3akf2drJdcaiQOMZPSO1Hg9l2lD6cqXZNNYC554YWGXKevn9q7AO7KSmUNmipOUh/n1jIPbFjGB+vPnxDLf9/1yxa0b8Yi4F1yACTABJtDzCPSOEn7pfX/L9fL9EAkBt6sZLncULLe6Qb+VYLndqv0Yi6SiCPJWbf4NWVl4MGrGYoxPCS4iBFQpiBV/c9PAfxQNdLhoIs9tvoPw7mY4/kYD1xRk1mqjfhj8IAyo81okuI5j6cvviT9iTWmPYMl0nfUHBclduWyDNnjcWwikm4dRQjBdXaDhc3tfo3fKyC1jlO7kENtJAttmGkB4RfpBbRryGJYUJqGa3Fxd+O8G1NWSZZButrBSlq4CcgFF8U+GDwa+qSE3exQXReeeSp/PTjF1khIGwH5HEok28douWjM6z+5ze7FcFycF1P/UwcOQkU6iyp0kMIYcgST//KteEl202EbNpNm4A+A4ex4u+nFv7t0HsTQI4RvAmAaVjm2hgR3JAiid3LwVprhEoavBQTOuv6TZ1kavUr3jkUsC6PC0OOr+UZSWyK5kfHpHGzEUMyMhCQkJcbDfORip/TVho5mO+wf5uFmFFGNoiL9Q519ZZ7dpMOYFYaCKLLVIJFDdhznp/OuEUeHH9vjZv8YoPysp8egUN6fZTIM4dA603zT0W+m39FvJZ7zES7+lloi/pSJ11dfZXl5P5YU4Q66/SwKLMJhhud0SwTOHZkA3fw03xYKxkJspl5eeWVTeJ1h4qM7Ss7C5mWJWfScc34Sof6b4ByEE2VBV3RD7mumZ+go9Uylw+6IF4bvD6tq+dfKcdW1jrp/ayIrF3fI1XcNmenfwwE3XckT3wvVyLXf3d7P/8YSB0LRMZJCb1lQSsUJNsBBO/oVSsm4RJhnE5NE9MQ5tFDvP+S0NnZNFqPWOAVTe56H9vb4L+F+sDkFM2Su79vXfSdupU+djumIxY7DfKMlLos62bSXk7jbwvSk17xH8hKxwLb3aaCJ4CbaRJYsw9Gy05NK7U77+vdR1CqvJKkc1pOyfh1/MHkcCEU2seIUmVggu22gxarOTXH2KsRVFUelFikMk5QWd+9X0HigKlllFFA9ukLxD+HBh30rZFRxt5dKYYn4HxhTV68P/nUB3pJCr1O+VsjVS0pT5mDFcsmBqPnsImzd+rPEIWUn7Oy30LrdAtKwSXNSS613ZRbX0jicLahSLcvNrUjyp9mtsJ4d8v4R81Wyniu9j9434+zIUJ/X6tN2HpfNGh8oasM9Jln2rBcu+G/RcBnRIl6Cc55RkvZisy+C36veU99vLmxoBdfZQBIMOWumga5qSGXzmY9DCvIMJ3GgErrai9vR5cWZFzJ13wy7PNA63G62Os6hvFmbmxCItWXkjar90awu5jXE0STM6aPZINAWoTbTHtF8w4hytqK8+j1YqZ7YOQHI7HfRcdqLpUhNahMEkmvUSExOD2FibaAIdyaE7xOVKKxw0G6LFJc1sN0fRse3xiAlrLNyDlkZq+9ct8Agzc3pHi+2OjQnzhPodO9oi9Tu6t++s/0gYqHnJB3rTpQY0tQhngc4D9SuGmMb6WRSo+f1WPK0taKK+tbgFLvI5uYPOSVcMCntaxbqbXFLboi2xIrfosJhTczrDjbi0NMnnTOhZhFwETF15HzXVfoEm4bqP8F4W2sHL908gykJCiThuTYHdw7ztO9Nq94UyUfwR7kl7pBY4JOzYZKukKNVFla41UVaaSXatB9p1xwu2aslEYdbHohWQ9/R7+MyZqc3EjbqN3LsNgjthGNJTSTywaaKBvrqof7aR0EVG5eo7q35v6HX334F+6rOoj+jWRxjsyKX4RMDXqNxJM2MDqjDL1i33IDWJZmQqz0nqS8GTmbi/lYIBk7u2yiNHaVatMnRC7qXIbY3wZ6JBjIw0rdJg5znKnon8vCjYBiTDHm+DleKfRLKIMzipgDRD2AR7CtUTogITDbBLC82mTqd1cnm4u8TAAql3HAlx5G4wi4SoO7RryEIu/GbQn9tZj6qqo6j4K/m1V0SjFmIi/J0k7y7TadBOaQdZ/+yRxR+Q9U/+NRd/aDBw/y5xljLgF8dD54YGiMe0eT9DhlFgZqHtFARa6Xm/u8Yh9xaXaJHlK/5InUzKvw8meuewpfiKfgqCm+EzinhJz86O9JYs2Gjms3R10rM30ipEkTuyeyfSQ1w/+dtw+CNZxCaRN2JWXdaRTp6zLmvHdVYRWXBECbP45WYpT9ywW3m9XMvd/d0cFY9J4/Jw+fYkDCRrBps1svvZS7H2pEWyl7AmJKvPb2P238+7gFFbrPHC90YwAWgw8jMkscGobLA0E80ecfqIPxZkjZtCsakGi5MapHLkHm74FCygSTdVZdtRZRqBUTiEd0qktuTOWOgr/giFyCpn7rxHsHrVe5Lo8R05MRTej5odqJLFH8FF7DQDwcp6h9BPYQKKB9UXGkkAkvtF5z6Dhj6EeEOuSopjSW7gtNiCZHX46H04vPZjKkeuhCkwZP7ARHE9sv8UOxq9NW8ENVBx4Z520bt61t3a+bCmjMbcZSPgPFeLGnJjfO58PZr/RvF3aFJWhxbF0vbq12SJLo1jgIT+GXp3e0I8qcWLSbw7hMOfn0IdnWeld5Ee0zIkSX1WRVqW818DAjRJ6fBJ+i0Q5hkVPB5/eZTEH15EAiwAhXkhRFloYJJ+HONKxK8ooY9ALzDCgIawhOWOW8zJ/zGBG5OA51wpVrzyodj4BxatwaTkSEYOW1H26jJ89DUVv60Qr6+cgPZKt1Lch61vvYHyegNet2Vi1s9nI+fO9moxKBskqb70VSzdelbcG1PwAl6ZnGKY09P0BT7YsA6f1nxjuH/EQ7/EoxMy2u2fVDhCLldbULHjbazfc9zw2IljH8fTj+YjNogW46j8BOv/WAwjpEgciVlFhcQ0iLjmceLQB+uwsVRi5N+AxJxCzJo+IWJhUKrHg9MHivH7TaX+1UrbiWMx76lCDA0iygnnZA+dk91BzsmYR5/Ho/l3y09r40METaV+V3ywGetLO8YcneImcNmF9Zt2QvnN4dNO4vKrnz+OtGAnnDJ39X3k+aoUv17+rtSMMO9lnzbzxs1FgFyerF9XIvbZNITctll7bvdTpzyGpMo3UUdd3L3tELLmjJZdYViRW1jUbsftox7DglHtZms/g3UwFjxfhD1/cSGnv/BTIQ4/KRyBP3xUh6S7aOApdRBZ8NDgEwlRoX5ImKJJIBk+TvyDm4QfB1kQiT/+6+jHuAWP3yeIS/IS6jzTwEdufkdFgz4YPm0qXBVnYE0I7x3eOoQGe2ZYcODrQUgQBm2s2ZieU45iOjGpSYPIRQuJcMLgWzvWE1G2RAzPF/6mUvelmA11X9aLcZzqbxmBCWlae9wXT6kCW/6McfIgvwKn6z8Ft3rr5QGs9Gl5voOApjgUzJ8Jc4kDw6dQfKpQJ1nXNGtKNg2E6RJ8Vk0UX2E0bga/ET7d5o3vhcCF0nXYR/EyhN/YBePJKpEXJnBNCHTzdzNNzUjPHdfhngwc/TBy245T3J8wg6B/H+8CQXoXlTAChVNt8NKESXWhyW00qxOpqRS3RfiujnQh97uFhXkoPWeiiRzk7pUmAwX9uutlQQbF/CPvYsBVK0bVliBh8iNID/YFacvE3EVW7NhUhvTp4yQBwZqJBb+1o3RbOZKmGMcHNN1Bbndj6Pdi2mASfPQCH537kdmorjGJFl/a24PUaVP/0SQoHUP1DyeicHhHxB8ai4yywdS7DdaBY2BX1NlImNL1MpfeHXeUNKvuYrXiJtgGkmhFf/rXVC9Z8wlWqW1XyDL1O8HVmyDT0PpV+iCh2P98COl9yYWcuPSKQ+Fv52PHHzbA9m9G702CeDdO/BPye910LIox1CYcw0vHEhKF44gLWSPeQtaIJNb5LJQp6nZNzPLZxxvfEwGypCve9D0d+8Y/LLuAu/HPIffgBiCgmObd3C7gWrDnxXn4SFYOHl68Bj+OQHxpqd6F52TxCImPY+1v8kMOxrecpvwrJbEp1CXywAISotI6LwJ5HEfIz/o69VDJD72IhRMGqNvKiufiAcxZ8rayGfwz9XG8/nx+uyJQRFyuOrHj+QXYLYhoIZexeOnNJ2HXvWML2U9vX4bf7zkbsqSwc8xTy1GU4zdYd+U83nr6Nyhvt3QmFq7+BSLSBmmWVOWmBVhzwFhQ0x/yYRIef+xXeWttKZ5VBAl9Zv/1nNlY+9TIkNedfxFcPotV85bhRMAO/wRivoaYqzPw5f2d4kbi4OqnseWY/7ECt2ctXU8DvX4nnLJ1+X3kacBbP1ukXQfB7mXKB7PfNRTY7C5JYRdwXYLx2lUiu72w5D2GwvzBPAvv2pH+fmvm89yt/CW3euUkLP6crI14cKNb4fPBrjkB0R3mUSsKn3oY6X5x6a75wfkATIAJMAEmwAS+ZwLXy/hjV2FQXcDRFCk7eR3Qy6Mhj0FeEepP10qCH7uACxBVQ7K7IXaSiuy85MTlNmHWDynZfSywkWobFUrFFlRgOZic0sdmciH0DflJBrlBGCi/OAqKtBJ0Tsnn++lF86VGfOOSzCbFY9t1LjGovFdQsv1nKBilkxotqNImQZVWpG+hbzR78rLgRuIHpEZHU0Aycjmh7PZtC28xgeuHQGvTWfx5wzLslsWfiFpGbrNqj36CFW/tDL9YSxWW6sUfsnKY9cgEpNlvJWuG49j4ytuqEflHK4uRs/5JxPrfl+EfjWaPOLFVJ/4ELSrk8xF/+uGBnz6JrDvtJCq04MSf38WWA+el4jXv4oPKDBSpzn/9au0Al9o/k4WLTvxJzn8SD+fdjZgfEOND23VWQQfw+w+y8cqj4jwn8cCtp7f7ij+pU/CrR0aQazWgqbYK7/9Jswo6+KffYWjySmSpViUk0BT/QRv0pxpHFMxGXtYACPG46098gjVbD8gdPI4VHxzFW0XZfh0Ovtl6msrrxJ/EsYV4+H/eI4opji8PYj1dO4r1y/vLi5GlP99XSKDxEX9SMOPZQqSRFZOnyYGKba+SVZB87Ip12JqdEvycBDSxFX9+xVf8ubfol8hLp/PtacHpI7uwUbXEOoDFmzLw1lP6fneOW/0nr/qIP9r59pD7pXKsIqsgZVm/5F0k67kIO7r8PqL+bP2dz3WgHN//8/Qn65A2+UX/ZN6+GQnQDM25y5bfjD2/ufrM57lbz3fUwIlYumxitx6TD8YEuovAwInzsZQv7+7CzcdhAkyACTABJtA9BPqPwZwnR0d0LOd+igFUwm7gBGg9RztwN2L/jvdRetL4xNrSxqBg6kQk+E30d362Cat3niEUcZi1dD5sF8vxzsadcMi+uqVApQPw2dtLsLtOQJaMOb+dCbsPOS/5l/w4SHA4M0ZNm4mBfyvB5jLBV6hvoFUnBbRbLQe0Gz9nKUaJrjbIL3fZaxSkr1E4IPJnL0Bi/cd4Z6/gD9R/IZ+fM36G3JQe7BPFv8u8fQMQ8ODErnfx6VcUY6ae4vboRIdwGu9prMKm90rxbes3OFEjCyLhFBTzeFDx3qvqgD+GPYnX545VLWliYsZi4ZspOiuEAzjheBx5BtYP4R7ydPEbOBhGZk99lS5fPzy9gkSSOMXqwoa8ohdhv42sbHZIVjYHd1Xh0SzN0qlTXMiiYvcHUr1CU4c++iLm5WsWSrFTf4HE/m9j8Z8kIaal9EOcLsiAZBzVikPvaWJBDFnC/IfOEiY2Nh5LhqZg1VxF7PgGu8nXapZiAXWlAft0As29P19J/dJiOMWSEPV6fxueVUS7A5+gtjAbyf7WMIaMW1HxX5qlV/JDz5Pl1d1qzpjYqXglPQVLf/mq7LbuACrrC1Xrs9N73lXFQGAkWeHM1qxwYmwoeH49Ercuw5pS6To8+MeDKNgwFUGc3KnHFVZaqj/B+/Va0gyyshmtXmc2jCbmaXe+i1//UXZbV7EZJx7NxlAS1cSlM9wuf4GNuvM9omg5fjpWs6iJjZuKtRSIfcXCN1Qufz79IB5NV3rW9fdRy/FdPkKdRsZvjUS5j3acx8LJfum8yQSYABNgAkyACTABJsAEmAATYAJMgAncfAQufozSSjLyIA8wgsFEe4uJ3EY6auV4U2QNdLMvPjLGDQuj+RRWv7JFCrIWpBPO0zQLfPkx3D9nPob31wzG3K5muYQFzTVleGdzic+F1Eck5IFLjVHmglu40lRyXprZ/hJ2KAHIAo7vweFtb+KwkC4MZsrCkpLNTT7RleUy+b3UKlYPgNJ1K5UsBp8ulG5cCZNOPDLIxElMoJsJkIXBARJWIhR+1Ea2NqD8GPm/7cjSehb7KpSCKVj4lCb+KKmCa6m8ggEop0FmYWmloMRKLC4xIYL/WsnV3O9lcUAYOlcsTQyrMCtiD+0dS5Y/qvij5U7LfxCJO142jrHTCS6ei1U6N2T5eEwn/ihHt1MMnoc/OSCLFudR+WUL0jKpVxSD5nNVyEihOD0GbtCiKX3Bg6qIU3/8LDwkAIk9pi9eTXvPxySd+KMcOzotHw/c9qEU44kSdaSULMaf1LZyxUKHBJwZP9bEH7VATAYepvP9e/l8q+loheOEJjDeS5Y/AS7YqCVZhbMxtHSRzG8nKi9ODkswbKmvVg8Vk/+8TvxRkxGbRXGPhpViveim7Rt8WtmAoYpQ0wlunqbzumtoCh5S6tQODXNcNsVsyiTLI+le+3TXcTyUPlZi39X3EQlSq16XRcTbqBEhng1C26XQqrrG8ioTYAJMgAkwASbABJgAE2ACTIAJMAEmcJMR0OSe/dvf61jf/67V0bEKbvxSmspwo/blaiM2+4k/9qxxyP+XQeQdsA11x/5Cg8HKUJILu9dSkLAXnsFA1SWcgqAW2zYr+QQYZtj7JyLJLoVYU3L5Y3Ic3uQj/pj6Z6NgXDbsFhOcZ49iz95yqBKPn/gj1RWsZv8jkTREdd+fl4mE2wWxioLRUt3KJbyv7BhGTde7DgoszylMoPsIRGPo//cgvjjcgFt1VhxOipNTqwoJwVtjtmfggZwv4FBlA5IDmg/oBvqDl22t/0Id+E4sKAxqRZI84Xm8MpaEnx+YEdNXkyeC12ywh+KzrFesVlKfxMLpZvx6yTqDjEqS5JpS2IrRi0HK7oBPD81t0MSQznBpqv1CrT2xIBux6pZ+hc7b6Ey8v1USBM47KKYOCUA+A/KJI0PE59H6B7e+Xl06btXv8F0XLF9kYUBfwjeT31ZriyYWpGYEdeUXbcSbxKMq9XpMQU6aYv3id4xe8bi/IAUnFMusEw0kAGnWU3655U0PBBFMWcbnBMtvxtD7HgSOSVZMJ8qq0EpijXRF6ilExs3nfD+UEdRiyZ49AYkkAIkYag6g/spY8Z7p2vuIYhH9URE1KcbT81NR9nzweFCtTcaWvApL/mQCTIAJMAEmwASYABNgAkyACTABJsAEej4BkyURNvKWZemtGXNE0uu2Ky5YM1Jv+liy4asPkdDtxrzOo7ugTv6m4+bOWIj8FEm0EZphT0jGPVnl+N3anbJYQm6QyBXb3InJQVpJLttotnf+kHjNyIeEJMPlKrk1kt23CfttOUWYO3mQmtV2xxSkZwzC2pc30UB25xZ73kzMydfabLNNwb/H98HyDWVSxefq0YxsWDt3GC7NBLqMgD1rMuZl+VbnONCqWhv47vHb6h2PSU/9wjfx6gR8O0uxwvDdpd+qr5bECyFtxFB50J3i5TQ1fwOPPJ5uJtdesST6xAQZ79fXF3ydXGT96TeyVUg/zPv5WMS6ZFdeQQqZe2sHlFysvSC7WNMK1B/5RBWwcGesKoGJOTrBxeP5Vj1IRrLmCkxNlFdikzNoTWJY+2WDaMUjiGTKkjw6Jah1jjnGV1YScIslfxCNfkoF2ImK2gn4cbKv6Ob56gg+UsUYKTaQWiTUSkwKfvXUbDRd8SC6f7px28j93Uc6d2hmRQyiGGuturq1XuoS5VWPSxNzlOKBuXxT9PKNnqFvLtrykNCmLFFmrQ9dxE1Xo3IUn08VO6UqDLryPnIcodhW8hf1vc/ORnLsN3jfpwW+GzFpE/B0UTDBzDcvbzEBJsAEmAATYAJMgAkwASbABJgAE2ACPZOAffhUzB3eM/vWnb26wQWgRpTu06x2bHk/8xF/FJBR/UdgzpQzcqwfwHn4L7gwLhkJBr0fNePXGJ8Snqroqi6HGBZIOFDvbEzXiT/KsWEZhMJpg/GHbUbxe9RcoVeo7kKd+KNkjho4GqNiynC4hVKu1OMbmnFvVS2blFz8yQSuIwI+I+IRtovUG/1gvXFpvUuvTCRbv0HF9s1Yv0cThdRyZMny9E8fR1Z/XyFC3d/OSlPFu7LbLhKafvrvYtwWj3Avhlpi0/FYKuTB8LP4/dw/YNai6Rj6w34w/+MbnD6wHas+0Nr68Oj0ULVJ+8Li4kGtzholJjpEn/XqxtetogVSdNxIrF2dKa2HKHviz9vV9iYOVaxYKKkXxTd6KAUHZRHm/eWL4HnqGYwmgS76B+Qu8PQBcg9WrJZNfmhkUEseNZO6Eo20nJHqlv9Ka+MX2LH2Zc393W0PIkeJw0Mu1oRIRMq3SMjLM0oQJCR3cZ6W9q9EoR0+qP6hymHCLt/lqs66p6lVvM5FqbAz3HQHD9kvEsESqTWSCET3mJC5dxfeR41HsPitI1J/KXbUQ5l07V1x+gqbvjTo+DZkjdViRPnv5m0mwASYABNgAkyACTABJsAEmAATYAJMoOcRcF88jh3k5Qomg0H7ruiu1wtr+kSMz4rritpumDquEc1u6r/XBafqVi0O40cKw1jGiy17IlJ3npGthVxo+47y+ffelofcMMUf4SjfXNLmTSfdOyKo9Y11yAjYSQDqqBWQJTszaN1RHMjK+IRzas8j0IsG1NvtFdk6qCLocaz4pSamBBStP4I1S47g/mdXoiAzwsHmliqs+NMBqcrUx1E0MrhFje9xo5G3YCVaX1ogW7scp9hkxm18YMGqACsZ37rkrbC4+Jb0dSznu88cNwBDKemEnKxYhJhJUFDWfUtIW03Ht2PNAc2SZUxOik+2xAkv4Fee38hxeL7BR39aho98ckgbMQXPY+EE37IG2UImeRqP4vcrN6Pp62/8YjLl46Vlk7XryGxDMglyUgyhs9h9qAHz8g3OZesXeH+PFiuonuIGtU69W6vHsDVmJKZTP45JlkPvbzuKvOfl+Do++Vtx6AM5No6Q/vVxsmaajJjeUqaOcotNFBhKwkv9B9tRn/8LJBqcwNqyDzWLMxK46i+1Iq1vV91HTuxYqbhEzMSSIjl2FF2zvDABJsAEmAATYAJMgAkwASbABJgAE2ACTEBPwFH5MapPq8FU9Lu6bN0SM5oFoC6j2R0Vtbm1MBO2wUgMNTrcy4IEGuOtcQoNa8S5S21IHehr6WMfNigin4BtLZpruIREa/Aem+KRTsd2iMcOni3YngQ5DlGw/ZzOBJhAOwRS8/H05GxE08Bz68Wj2LK1VBUGdr/+BtJWvxjgii14ja3Y89qrcvkB+NXP80MKIwH1tLai5XJAakDC2a9IZEjLaEdkCCgWNCFa94hSXaAFzR3ZjtoDb2PFJlkQE4qOfYZi5PipDVdb0OJqabfiFuq348rdsMsCSLsFjDK0NqGWxJ/ApYlEoVbY7cqXBYk0mSSU1EgizYmti1B25xrk6d3TXXXiz6uV+DVyjXo3bYEHUVNi7hTissmu42rexprSeBKY9OKWB6c/WYf369Ui4ooPuQ5yM1sHQHAaKlk3HcfSNw/g9bljfa6npupPsEKOa6S24AfqWuBKhPfR6V1vYLcc0+mBBbOR2JlzGtgaTmECTIAJMAEmwASYABNgAkyACTABJsAEehCBvgnDkNrytY8BUNQtNH5vNoljb55vXaoWEEXBXqpOnpFCvsTQ2HsyDXwJBh+hFsECqL8WOiZU1p60z3RDd8bdpkXncTaI6+rk/w50LCkxEiuANpyr1RRJun6u2WKLufkuzGsGkyu+6QgkPyRYlNyt9TvtbmRlZ5N10Mvy4Ph5vP/JF1hCFh3hLPWl69RYNWN+/gxZS4RTSs5DliQr5inHldJiUkfiXhIhzK3nsW/PAVWYOvHBq3i2fjbWPiVbTURwGMOsqrUkhZwRgyH5yAyGRdpNvHweO/74G+yu0eVMLcQrRYLwoV9a8eeV8/C+Pt9tKbh3bDbs5laUl+4kwUbOf6wYi59uwEtvPkn79HWEv26234MZBeTOLNqMptqj+LRCFmEottGqF57GjKVrMFp2/ZecV4jkD5apbuC2LH8a5fmFuH/YALQ2ncW+TXoLGbkN7lA2VFo7o5PH4rHEYmyRBZ4TW5fhuaP5eHgCiZFuJyo+eRvlfuKPUFpz2dYJbtEpeIzc7i1VYh8dexvPzvwcD/80nyZLfIvaik/wUYVm1aS2+h/qms9KpPeR56tS2doLiCFBcFKaIrr5VMsbTIAJMAEmwASYABNgAkyACTABJsAEmAATEAnYhk3E9GG+MJqP7cTmI81ISh2MfJrcrR8lt65ahFIyuLBlTaHwKcE9g/nWePNt3dgCUFQfCDY8ogxjixfXu+8U9kFCMl1ylZII1PZtKAXIBcffO96yUDV3vFYuyQRuAgIU72WeXvxRuhxzN55+Nh/PvV4qptSfaICHBKD29AaP4wCWbj0u1ULxTB7NCiEa62PpyMc98cE6VWgA+mHGohcxOlmM9iLm+HFBISqKX8b6UnlgvmIdDv04G3l3ttcy+QAhPsjwKMwlnGNRTKEjxVjxlsRPqXhowTOYJVhaKQnyZ8vxD33EnzFFL+DRsSR6yfvzJkxF/ZF3sVSt7wA2HsjHwnwh7k4HFoohM3ryZKlg/gQ89MhZbF22DAdlkWnja6XIWim7guudgnmLn8SzL72tHqi2tBirfLum7hNWEoXYRT4pwTYEl38voHbuMpTLWVpqSrGe/oIut2UiVraU6Sy3xAm/xIwvn8ZGcp8rLcfx/lvy9ask+XwOQGKs0DNNghJ3R3ofeRqw6aV35ZrH4leP+wuCypmnLJ2ZtSEfgT+YABNgAkyACTABJsAEmAATYAJMgAkwgZ5IoA2flZTDSQ5lnE1WjM3X97FNsv4Rk25siUPfq2ux3utaVNptdfaJ0saOnEdRH2qA82ozLqgu2OIw8A5BOurcYonR6qiqaQhembMO1brZ98Ez8h4mwAS6kkByfnAXajFpY0UXWeLx6qvg8BvzDmgHuQL74AVNJHhg9AC0OBrgUP4anXD8N30jyYvnErkya6T9F53wXKVEz3l8qouRc/9zvuKPWKxXNHIefR4P6yYtlJ8O8WxRDhbxp24A3q+sh6xeTujSArC0kJjy4iw/8ScFsxavwTwD8UcQE058ogkeiZOeR5FO/FEOlTjycSwkixVlqT10FqEe6Uq+cD7NMSkoev4ZqFLb10dRq50qRN85FmtffQEP+MUtkuomS5rnVmLtitnqocwx4ck/YgGyxPnpm6swq2Csdny1pn4YQ+f79TXLMUJJIwFGqr0ruEVj9Nz1WPjUg0i+TTmA9pk89nH8x5o1mKXOrjEj2kCQifQ+OrH1d6rghQKyYPua7g3lPiFfqI6L51VLN7hbUC/cOxcb0MLfk9rJ4TUmwASYABNgAkyACTABJsAEmAATYAI3OwHnKRyWx2/sY4f5WP8IaBTZx1n2Bop3bceO7YF/xcWbsO9Y401NUuF0Y0IwUVwfmintFAeNXNi3vxapE4WoB4GL8+heaN6HLLDcEpgn0pS+twuz/6ULyFtZggtTBiHBgGh1aVmkVXN+JsAEOkhAL1hk3BnCQofkb20Y/1tJpAl1TE8r9A6zPnplET4KkV9wFbdY1D36YeGa15BMx9MEjUzkpKlyhF8tNGj/yIN4f+WHfumd2TQjWRfrprVVoBREBLqqI0ixcvS5PBeP4NdL1mmD91SLIGA8lH+3jmVgO3U14t6RmsjjnzN57INI/ECOt0NChP7Y/nmV7VbHUez4cxU8V8wYShZUWXFBSsVm4oFUYKP4RUDiXHMrhuqEHEEkmvTUC5j0BLmOI3OpVs93MEfdipi+0lXSVLFLOWSAgYy2I8iaOQY5k5+kv8fRepnqdlPd5lsRbSG+wjSMxiOaYOKWqhd60TXc6NznTMZC+vMI/Wqla918Cwk9MYgWLY2c2KNaCGnH1B87svuoFU1ffaOB2PEyfr1D2wxYq9+JFQt3iskPLFqDSRR/yVH5Cf5MVnlFRU8GZOcEJsAEmAATYAJMgAkwASbABJgAE2ACTODmIFBzsETtaHpKvLputFJdcdQoWUwz3Toa44fFBd3f03cIQ0838BKHUZMGqe13Ht6APScDFT3XuTKs3Vmr5rPljYPdQKhRM4S5Ys0YhyQ1bwPWry+T3NGpaYDjsy0oPklu4sSBNt0OXmUCTOAaEJCFDrnmqq9Us7/AY5FVjibIxIPCxYReepF1ROgcEez1FVb8C5pJGOjqRRAclOXgab2UpaRKn02nq9SE5MR4TYS5TPGL9OLPbSPxq5fXo6gd8UetTF4x/yAEaHNoLv51CdtNxz8hy6oDOFhRCodLL1sY5Q5M8zQ1oPar86gnS61WwVKLXPdFx8QgNtamij+CLFL72QG18D1pIYRFNRetXHGivpbq/oqsW2TRLbqvXDeJT6L4Q9kceuZkhWR09iPmdrUVDqFf9NfUIl3p5uhoxFC/Yql/kvhDPXN8gYNKmykelV38rurMfUT8DKyNlEO0/9mK07uKcZDOKS9MgAkwASbABJgAE2ACTIAJMAEmwASYwE1KgLx9FcuhVwQCpTsPgubMGi4mWzJyckYY/mVljUBBVmjxyLDSHpTYBTLI90vDljUR6dvPoFpuRkXxa6hOG4Hx2YNhMblRd7QM+0/qXSgloyBX51+pM83vFYdJUwdhNR1fXC6WYOULx5GTlw1bVBtqjh5CjVMekGTXNp0hzWWZQNgEYu8S4o2cFfPXln6OlgkDDFxvkXBwrFSLx5Oagtj25HBzPJ5etRKOplbSCIxFjNb/LsWKP0nuzhLzZ2NW3gCyTAFihez/0AtIR1DbNBuxQSYfnP7/NWuTyCUNY1SxaRm047i4s2XPJ3BMvhv2gG44UabEOKKcdyf2kyvzoOId2TJHSEktxCvPTzDkKhfw+SBdR11OnHMiJ85YQGk6cUA7J26PaAET0ES1JmlFfyo+Kj1OFiQj/XLIm61ncVA1A01BcpzUKMeR32HFDsliZcyzq1CUaWCZ1fIF3j+mVDsSQ/vrOqQkG3x6HOVYuly25Br7S7xVJJwD/8WJil1H1MQxmdpLSae4URye37+0TLbWyscrGx43PF+ny7arxx46UrPk6vh9ZIb9Z2uQSIKaIKYZLu7zWL98HeqFnYn5+NVP8xHj8cBMFmeiDZKBGzrDejiRCTABJsAEmAATYAJMgAkwASbABJgAE+iBBFzYs3G7LsYPdZHG3Vdvj8OCqYMD+msdMg6T8rtovD+g9hs/ob0hzxugh3EoXDRTZ4kDuE6XY9vmDXhn4xY/8ScO0+YVGbpp62hHbVlFeCJPG7ATXMJVlH2M3XvLNPGnA5V7v/N2oBQXYQJMIDoxA0MVDF/vxJpdXyhb6qen6SjW/EmzMBgx9m7N0oVi/Rza/q7oN3Tr1u040ahJMOa+NiTeOQD2/vGGf4k/1J4F5jsonz2e8pN1kfCkNdtwD7kgU5b1K7fDYSAMNx3fhVW6WEH3hHJjp1QWxqfZfrcWZ4aEoPUGXOpLN+NTta4UDE2WxRDitV4RQG6bgv+IQPwRHLklp2tu38r/9AYqDAIueZqqsP6PulhBWQN0ljAenD6wC1sFX65b30XZcc2yKzZdFwGwYh22VuoFf7kzHnJztvplTVxCPGJkkSH2Lq38wdeLUauZhUmFqezWZa9qbu/GkpWMXtcIdb3EpkB9/TjwKsoCK0fl1jew+2sFej7S1Mo7ya13PO5VLXFKsbFUEkWVIwmfTZXvYlWpJH4BFI9IJz516j4i5coe4j6xD9SJslHxSBbuJ8ofK1sfpRFjXnoGAa/zDEr3n4IrxCuN29UMx7laOP3vvZ6BgHvBBJgAE2ACTIAJMAEmwASYABNgAhESqN5OY0dy7B9T2n2YPnGQWIOrcgtWbj4Ep2gK1Eer9Ttt7E5L5DWFwA1vASR2JDoZM5YuQOW+ndh9+IysDgojdMrJNyMpi5TA+0aTZY7S9cDP8GAE5hqY/wx+kXQU+z4pQfVFcvemLnTcIf+KSQUjUPPOS9h3UdgRWF7Jrt/TLyGJkhvFXfp0JW/gpw19bwlM5RQmcNMRIEudx54ai1/LAk8txSD5aXU+Zk0eiUQyMKg/fQTrP9CEBmAkJmXrLFIo1s/BPZp1UOyPJpCgpB/xD5MoWTT4LmbkTH8SG5e8LSWTOLX46QO499FHKB7QAJhdDThBIsdHFTr3bImPIy8tPGsT32MZbPWKx6SiTJRvkqyA6ve8jOcuPoiHR9kRE30r6g8U433dsRMLHkSyOCBPLsqqy7UKqd2HPiEeokszLdlnzf0tYgZPQF6mxNU+9kkM3boIJ8RMZP3xwiyU5z+O8T+6m6xSWlBPlj/rd2hWMEAKHvvx3T5VOso+xKf1UlLibWOpbmndbM/GrGHrVIHq0z8uwtmcQjw8lqyvrtK3QNMX2L1pp2RtItd4/3OFqsVXdPI9JIx9KMfgOYIVc7/A/Y9OR1Y6iXkt5/HRynVyu4XC/TCvwM+KJ9T10jeFfMxCbduW5U+jahL1O0vqd+WudfjomCLAAPc+OwWxchuFj85xi0bW1JH46C2J64mty/Bc9RQ8Nu4e2EnXqz+yHev3SNeCcKyY/CeRpTd+6ux9JFQabPmH7t4wsN+2Z0/GCLndwarg9BuDwAWKv7j/cCP2l8ThiRfmY6DfO5j3QgmWr5PiJJpyZmLJZOM4jjdGb7mVTIAJMAEmwASYABNgAkyACTABJtBZAuf2b9C5fovD41NHY2A0uXE79xJ2nPaQ4cfHWL3sY9jTBsErzw92e91wX/XSqLvwjxZhIrZ+ofEhdfHfp+7ouSvhaQs3Qv97WZE1sYj+vHA1fw23yQKL10WzTvvAersFpiAnN4HEm6XaBPAgPe2D3HnLkRtkr5BsHZiNwjnkeoouNresOkZFyUrk1XpsE8UfyhgzCHbdAEiw41uHTMHSZVNCHFHY1X672qmAdzOBG4qANkE8tHuw2JzH8TTFVVmjWNLUlGL9K3rRR+n2ADy94klfiw56VnSF5KIb4lYOBnP/sXjpp2ex+K0Dcto3+HTrOp3VjZqVVkZiyYL8sNoSLhf72Nl47MjT2FIjHafl2IeqOKE/MsjK5+lJmgDjOKcTpSjj7g9kt2Y+hXw39CINSEx4euls/FoXQ+hE6bs4YXRKqJoZi3+pik9KrWbdc9NXjiNh7anlOP30IjWWTX1FMX5foZT0/Rz66AsoSNedYWrbo4sfR/lL78oZv8FuwSrHt5i4df+z/wtD9SKJkBryeqG2zXwB5XOXqSLSiT3U7z2BlSdO+iUe9Xc/10lu9pGPY8bRI9goW2+1HCOLOPoLWOh8Lyz0E7YoU6fuo4CDBEswuJep30V0vfByoxNoxGESf8SldyL66u5hpWemhExk9S5DJVlDeo8dRzMJQFZlJ38yASbABJgAE2ACTIAJMAEmwASYwE1FwHGYPHqV1Kp9HjVjJok/wqYJWdN/DdOuDdhWIXl+cZw+o+ZzVWzB8iDjQGomeSVn+kJMSrP4J/fo7Z4jAKmnyQSLNQ7SaewDg/EGNWenV7y1WPvbDXAIFdnysGjeOET1MiEqyherk+IQiXmEfLf47hOSeGECNx8Bco9m1Q3ChwXADMGeRPgaSByqdw9mVNiMrKKVWDK0FFv+SG69DLIMJQuUhwvyIYYdMdivJAWL96PsD/aZEqRi+8gn8fqdI7FjWzE+PeYrrEh19SOroCdRkJchuY4LdgA1PRIu0ch7fj3spcVYv7VUdmvWj2rSrFBGFDyDRydn64QnGqA3cFWnHj7Iin/4F3P/kXhl9QAc2vEeuSPTLE/0xZPHkmDxIJ2TvvpUeT3U5ULuzorWr0HWnz/ERrLuajEonjgsHw9MfdAwfk/0nflY+2oKyna8i/cPBLpKi0kdi4cfeRA5d/qrP4EHCrheolMw781VqCz9EGs+UIQ/XbnbUvDwtMfx45wBukRttXPcojF67nokVtB98Cej+4DcvhVMR8GkbMSQkBW4dN19FFi3lBJzF7lINNgp9JuX75uAF82XGuBsbqOGeOlfFKx3kLs+qzy5pZ3muckdr6w1kzUbxUY0zB+HzB/FobKsEbhyHBeap8LKCpAhKU5kAkyACTABJsAEmAATYAJMgAn0dALWeMGRvjSKlzrlGYxP0Qs1fZAx+Rmkjq5HddUxVFXXof5io2+coDAA2cL8TRtGVTdMln+6evXq/+1Ia8/WSicjccDAjhTvGWW89SQAvamKO6njZqIwN1kyNZN76Dy5F6uLD6r9zSpciIIh+otX3cUrPZhA/flzYu++7/vlirsDI/k94Ly0tjjRSi7LBKscs/lWxNwWA7PhgHc3d5bcxLW0fAMPtcns+RaeqFsR29doOPxatMuDVjp26w/6Ifof9OkRuER3D5erdGyXcMxbEG3+Luxjtxx/F8+9XorEh17EkgnGgolAqvVyC51v4vkP+XzH0Pn2NRsKDvRKK5pavoW5763wXP4W0X2JT3S4hYNXK+6hfrd8TeebzrOZ3ORFfL47yE1plXgf0HmONn8bNnOlrPB53d5H+kZ2YL13VO8OlOq6ItfL94O+Rxcq96J4+0Hondoq+039s/H49KkYGPJVpg37V72EUtEc34LpixYiNcijzXvpEJau/lisPn3afBQOi1MOxZ9MgAkwASbABJgAE2ACTIAJMAEmEAGB6/H3ZQTNF7M6PtuJqltGkEv98H4ber1t8LaRRzA3TV78jjxzkXcuZfF6Zf88ND4kLB6vGYlpidfWYEQ61DX9XznPKcnhuVFnAaiTp6Nm1wpsrtAPkVDcn7RM2IQYC6ePwqGfit57DBYsnihbJ3XywFz8hiKg3JgsAN1Qp40bez0RaD2Pt+b+RozVc++zqwLdpV1PbeW23FAEWADyPV01u16j9xqyyAm5xGH68/ORGkQEcutj+wx5BHOyG7F6YxlMvQ3E1CsenxlbhnlIvvfGjsOiOXk3/It6SKy8kwkwASbABJgAE2ACTIAJMAEm0AkC18v4Yye6wEXDIKCc53AFIPZHFgbUUFlSJ8/HNM86bKtUBks8qCPhp86vkCkpD3P+bRyLP35ceJMJMAEm0D4BD8pWSuKPEJ/ox/6xctqvgHMwASYQBoHmk9t9xB9L2hj8JPce2Mgto6PqEIpLjsq1NGLzfx3F0ukU+zBgcWF/cZmaOmF8JrzlK8RtL4k97S1B8zS52yvK+5kAE2ACTIAJMAEmwASYABNgAkyACTABPwIsAPkBiXyT/A9OpVmweYL/wVOoqSV/+S6yCPquDW70gS1hEH40ZjRS+weZJhv5AbkEE2ACTOCmJBCT8zgWFuUj9qbsPXeaCVxjAlcb8F/FisADJJFb2xnk1lZZrLlTsSjJiuXrSqQkivFzzpuNgX5vks7K7TgsWz+b0qZiOMX0ab5rHPKj3TD55VXqbu9TMNu3Jg5j65/2QPF+JsAEmAATYAJMgAkwASbABJgAE2ACfgQ6+FPcrxbeRJQ1EVm5wh/DYAJMgAkwga4lYEbeoo3IM/Ae1bXH4dqYwM1LwF2ns16OyRNjGvrTiEoYgVG9S3BYDCfnguc7yqF/k2w9Q7GDzqjFRuUOFtetKdnITVGTw1sR/Db30lceXjHOxQSYABNgAkyACTABJsAEmAATYAJMgAloBPiXtcaC15gAE2ACTOB6JcDiz/V6ZrhdPYRAPVkxK0vO5NFBrG36IP+5hRh+xUvxfCywRCslhM827N+0CU5dkleLvSmmOslqqPSvRxE1aAoKRiXqcvqvevHZuiXYfRGwpE3B3OkjgrTHvxxvMwEmwASYABNgAkyACTABJsAEmAATYAJ6AiwA6WnwOhNgAkyACTABJsAEbjoCzag+Re5rxSUO6Sl9xDVvazOam11wC5Y+t0Sh3+1xJPpYYPURfqRSF/ZvRSkJNsGXNlTt2olqwT3cxWPIJwEouHNcFxqapJpcZGIUFbxS3sMEmAATYAJMgAkwASbABJgAE2ACTIAJhCDAAlAIOLyLCTABJsAEmAATYAI9noD7a1wQ3bpRT22Z6OuqxY7i/0TlRU9A1y1JI1D40BQk6NSb5tN7sb6kNiCvbwLFTKSYQvt3Ur4rp3DBNQXpujp88rY24ku5PUkD43128QYTYAJMgAkwASbABJgAE2ACTIAJMAEmED6BXuFn5ZxMgAkwASbABJgAE2ACPY4ATQdSZwQ5S7D6lQ2G4o/Qb1ddOda/vAJVTsW/mxfVZQdlJGbkT38EqUEA2QZmyntcqDrfHCQX4G6og2KPNDDRGjQf72ACTIAJMAEmwASYABNgAkyACTABJsAEQhNgASg0H97LBJgAE2ACTIAJMIGbg0Bv327ahuShcHoRpk9/DLlD9JY4Lmx782NZpDEhfeRgsWD61PnITbOq4o1vbbRlS0S6nFhzsiFgt5LgOH9GXo1Hok1yR6fs408mwASYABNgAkyACTABJsAEmAATYAJMIHwC6oTP8ItwTibABJgAE2ACTIAJMIEeR0BwuyaIQPQ5avoCjCcxR1lS0wYj6+Re/KFYtva5Uo7Sk2NQMMQKa+oYjJ8yAqOyKD+5kwu+UHyhIWZUn/TA++UZNGMwtCMopdpQf7JR2ogZBDsHAFLA8CcTYAJMgAkwASbABJgAE2ACTIAJMIGICbAFUMTIuAATYAJMgAkwASbABHooARJ/LFlFPuKP0lPrkImYRgKOsnx5QRZ7ohMxaniylNzO1CJ7quwg7soZOBQ/b0qFwqfXiWqnlGAZkgTWf/RweJ0JMAEmwASYABNgAkyACTABJsAEmEBkBFgAiowX52YCTIAJMAEmwASYQI8mkPE/EoP2LzVntLrPVVMPt7oV3ootQRaKyFHcuUuBCpDXUQuHXFX6XXq3c+HVz7mYABNgAkyACTABJsAEmAATYAJMgAkwAY0AC0AaC15jAkyACTABJsAEmMBNTiAOA+8IEXdHb+HznTdyVrcnIkkuVfel7OpNV4uj5pS8Re7ifhiiHboyvMoEmAATYAJMgAkwASbABJgAE2ACTIAJGBNgAciYC6cyASbABJgAE2ACTODmIGCKR2qM0tVGssxpUzYCP7060ed2C/R6UGBmg5ReVtzVX0p3BlgQtaGmskHaGTOY4/8Y4OMkJsAEmAATYAJMgAkwASbABJgAE2ACkRBgASgSWpyXCTABJsAEmAATYAI9jkAfJA2JU3tV8X/q1HX/laqyg2qSbUB85AIQ+iAxVT6Wsw7NOj0JrfWobJGqt2cN4vg/KmleYQJMgAkwASbABJgAE2ACTIAJMAEm0DECLAB1jBuXYgJMgAkwASbABJhAjyGQkJGp9sVbuQV7Tga6Z3Me247dOm0oa0jHYvTYBgySj1UPR7N6WDTXHKXIQNKS0cG6tdp4jQkwASbABJgAE2ACTIAJMAEmwASYABOI2HMHI2MCTIAJMAEmwASYABPoWQRM/cegIKkEO2SBp6L4NdQNGYP87FSy2XGjprwEh0/rRKH+9yHnjo69RkbdkShaDnnhgaOF3M3ZhFg/XlQfUeL/DEZqB+vuWWeFe8MEmAATYAJMgAkwASbABJgAE2ACTKBzBDr2y71zx+TSTIAJMAEmwASYABNgAtcVAROy/u0Z1Pz2DVTL7XKePIhi+gtYYrIxd9bo8N2/XfXCe1WpxQTTD6zIzRkDy12DcdcPzeI+799O4a8X5TxpQ2ClVa8+3hBtm0z82qpQ5E8mwASYABNgAkyACTABJsAEmAATYALhEOBf0uFQ4jxMgAkwASbABJgAE+jpBEzxKFy6AJX7dmLH4TMGvbUgPe8+FORnRhCfpxHFS15TRSW10t5moMJAXBIynH4PS194T82qrKROmY/pw7VYRUo6fzIBJsAEmAATYAJMgAkwASbABJgAE2ACxgRYADLmwqlMgAkwASbABJgAE7j5CPSyImtiEbLGt6H5by64v/MSA7Laie4Dm9USIQ8SebwuXDAqdcVjlBoyre6SECGIBaCQkHgnE2ACTIAJMAEmwASYABNgAkyACTABHQEWgHQweJUJ9HQCvX7QC1f/ofrh6end5f4xASbABEISEJ6JvAQh0KsPrGJsniD7QyQLkpG0kMhjisNPpk6FuxeJQZ1ZrnpgsrH40xmEXJYJMAEmwASYABNgAkyACTABJsAEbj4CLADdfOece3wTEzD9wITv/vHdTUyAu84EmAAT0AgIz0ReupqAGdYYC9xuIErEa0FqVnZXH4TrYwJMgAkwASbABJgAE2ACTIAJMAEmwATCIMAjH2FA4ixMoKcQ+Kde/4RbbrkF3n942RKop5xU7gcTYAIRExAsfwTxR3gm8tLFBEyJmL5gYRdXytUxASbABJgAE2ACTIAJMAEmwASYABNgAh0hwAJQR6hxGSZwAxMQBjzNgiueTnrjuYERcNOZABNgAkyACTABJsAEmAATYAJMgAkwASbABJgAE2ACPZ4AO7/v8aeYO8gEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABNgAkyACdxsBFgAutnOOPeXCTABJsAEmAATYAJMgAkwASbABJgAE2ACTIAJMAEmwASYABPo8QRYAOrxp5g7yASYwP9j722A4yyvPN//km7shnR8peAOtFwSRYdILgujEClrpZB2LC/yjkWQJ2LBBUphXyJmnAE2V3eHMDGUy9c4ATKrzTUsnkGwNhWREqw1gwhyrsW1nJGoyBNpQDFiLQ1pglR24zREuqbHbht1vPf/vN/9qW592bLP67L6/Xg+f+/X857znHOEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQuByIyAKoMvtjEt/hYAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBS56AKIAu+VMsHRQCQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBC43AqIAutzOuPRXCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBC45AmIAuiSP8XSQSEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJC4HIjIAqgy+2MS3+FgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIFLnoAogC75UywdFAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAELjcCogC63M649FcICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAELjkCYgC6JI/xdJBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBDEqo+RAABAAElEQVQQAkLgciMgCqDL7YxLf4WAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgUuegCiALvlTLB0UAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAQuNwKiALrczrj0VwgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQuOQJiALokj/F0kEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQuByIyAKoMvtjEt/hYAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBS56AKIAu+VMsHRQCQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBC43AqIAutzOuPRXCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBC45AmIAuiSP8XSQSEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJC4HIjIAqgy+2MS3+FgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICIFLnoAogC75UywdFAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAELjcCogC63M649FcICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAELjkCYgC6JI/xdJBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkLgciMgCqDL7YxLf4WAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIgUuegCiALvlTLB0UAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIAQuNwL/5vz58/9rJp3+bTA4k2ySRwgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCYIYEvhwIZJVTLICywiSJhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBIbB4CLhm29RsNU2zrUfyC4HFTMC0mJP7ZTGfRWm7EBACQmDuCcj7Ye6ZSolCQAgIASEgBISAEBACQkAICIHLkYB8X14eZ908z9n2ViyAsiUl6YSAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASGwSAiIAmiRnChpphAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASyJSAKoGxJSTohIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAILBICogBaJCdKmikEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBbAmIAihbUpJOCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAouEgCiAFsmJkmYKASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCIFsCogDKlpSkEwJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhMAiISAKoEVyoqSZQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEMiWgCiAsiUl6YSAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASGwSAiIAmiRnChpphAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASyJSAKoGxJSTohIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAILBICogBaJCdKmikEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBbAmIAihbUpJOCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAouEgCiAFsmJkmYKASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCIFsCogDKlpSkEwJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhMAiISAKoEVyoqSZQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEMiWgCiAsiUl6YSAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASGwSAiIAmiRnChpphAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASyJSAKoGxJSTohIASEgBAQAkJACAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAILBICogBaJCdKmikEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAiBbAmIAihbUpJOCAgBISAEhIAQEAJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAouEgCiAFsmJkmYKASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhIAQEAJCIFsCogDKlpSkEwJCQAgIASEgBISAEBACQkAICAEhIASEgBAQAkJACAgBISAEhMAiIeBaJO2UZgoBISAEhIAQEAJCQAgIgUuYQAR9P+tAcGoK3kANGm4NpO1r9GQQwTNelN7gS5tm4Q5EEfpgHCdCJ3B2WSmqbpr/NkUnw4h5ffBm8SUTi0UxNeWGx5NF4plAi04g+GEI4ZMheFdWofRaT86lRMf70XZwFG54UbWpAQFvzkXMY4YYgoMDCMMD34piBGbQv3lsnBQtBISAELhoCCz4u5nvn4nYF5Cf3csQUY4v3B4P5udtuPBjgXk58eejCL4XRJSF599QCv/VaWo5P4HhXwfh8Rcg74s+5F89t1QnxoP4+DSQVxiAL10b0jTtYtqt7onh4xPw+EpQWnhRDW4cmGIIvR9EJOZCYXEAHjGTcLCR1UuJwNw+pS4lMtIXISAEhIAQEAJCQAgIASGwQATCb7Wj62hQq63y6w0Zao1hcH8ruo6rJCV4+InN8Fsj+hgiESW2yHZxwevNXWERX3oEXc/vhd7yIAI/bIJ/Xj+eJ9DxVAuG2YhAVSO21JVmEGZF0fPjHeg5BRSsfxgPrfXHN30utiKjaH2pUy/pPQ+eeLAqQ3tSV+jJy0c4OIIID4+0+/FEU2XOZaQueQ72xk6gc38nFUCAe00Tdm5Mr5icg9qkCCEgBITAIiWw8O/m4X94Gm1HwUkj1Wi6fwN8Gd69wYO70drHJ/mKuhm9p6Y/KQs8FogF0fJYq/Zu8lU1obluBu+m8xEMHepGz2AIld/+DipXcDx0LoT2l9u093Hl/dtRfyP3TQ7g8ac6ULyuEQ23lXI6BJfzk+h8rUNLF7izGU3lczj5JTaGfc/pffOyb9tm0rfpT9gCpIiib2+rNgZT44ckBVAshlgurbjCBVfcNR7DcNcetPUBm773HZTNcIJKdLwHu1/sYUu8aNq+DYHZDotz6ZOkFQILSMD6XFzAOqUqISAELiCBP54/j88+48s29scL2AqpWggIASFw4Qi4XJ/DlVe68Lkr4r4iLlyDpGYhQEHGK2/oKhT4alG7Mj89k9NB9GrKHyZZwZmhjtF86PAe7D54In3exCNLqrF9xwZdmJF4jNuRcBjR8ykOOHctcaGyHLQSUTuD6OGM2A3FXsTOORMlr3uv9aWtNzm1vSd2fEhT/qg94ah7WkWJa4med+pMLooxlSdGq54JPXOGv66ri1DB4wMqzfFe9H9QjFWfZ+5M3K6gNY3PMRPWW4zGdQXYc4jnLtiJ3vEy1BReJBKIiTAmjf4XXutos7FPfoSAEBACQoAEFvDdrPGmkuAwlT9qiRyfmtZqwXWl20hMq1iuOYYO+v4Mfy/GsYBqrtGjDC2f5tAVUwi+RQtXjlc6f9qHsr+uhYeFfoHZ1IQM9+f0/AOvv64xGz40ig2GAigWGtPSqBQF+XP8bpyy1SJfuGrmY4HY8T489myX3on5+Etl4nZOesnUQnMMlpd0sqLoNiboZNu0RGVY+MheKn9OaNnbf7ID4fseyTx+nraiTD2ZNrMkEAIXPYFcnvsXfWekgUJACGQmoJQ/Z85MI5HJXIQcFQJCQAgsegJKAa7+X3XVElECGWczNkmlwuCYtuW7qXJGbqwuxIURPTmKvnfVx58HZbdWwrdIv91CfQegf8IC1RsrM35Mh97ptYQO1evL44Q40TOf5nYalmZKzpmb/60FvTkOG4Zfa7UUNOlLd3OW5c4ZzbIc6T1sFOtG3bri9FVoRxwXBJW+OS3RMbT+pNVinV1eNQO6BdOLWyqx/cn6uPNcdCtnbx/SZ/x2vz6I6hlYEmXXxtxSRU+FNMGXylXkTy/kGjvcij0Hx+E2FG4pazk3hcDtD2PzrdlZYoXfH0X4LAWbeQEE1MzsXJbTIQx/oBR4HhStCsCbRt8f4wzkXBaXK8frSBWezSznpJnNCa2KRhD6OIzIab29riVe5F3nR36WWKKRMBWakzhrdNflzYPfTyVsGi5xtdONonJxOBmJadeCR9X9pZm7PNLcZH2sFLKZz01cG6wNumd6V3fPNKPrwipnZivmNYmlPpTeOM2M/1mes1xbmFPbHIWb+TLxzPU+QabrOZHL1V74ltOlZ5ZuOmd1LTv6HT1OV6qTvA6zOZeOfOlWF+7drLdg4mifNW4ora+i7ULmxW0+ujhpI0kWnzHrxTkWyNjkrA/mo76pFgPPdgOnetA3XoPaFbDeeS4XSY13o+OYUpkBlffXwpyeM3XurFGLF4EvZfkgNnJM+8NqzXN0gpZbHacqtfdIpnxRXgE1dbXwO5oy9ZlSY83j8vF05duNcScpssweZt8+uzQ9j2/NFmz5wx7sNZRAPS89jchdzWi4ZZpnc/ZVSkohcEkRMF8Dl1SnpDNCQAikJqAsf2QRAkJACAgBnYB6JnqWXik4SGBqchTdh3o1Ft7TRShdJG6eQv+8Hz19+gdo9Poy3VXHHJxRJaAcp9mDGih7593/ehjdptXOkgqsuSHxE9fZoQn0m5ZCKq1yTeJY/NVNePjm9O96F33UD+1tQY/y58XFt7o4Tgmh77X/Ll3GdSOtvXcu1nxQcpWcl2gQ3Ud1QQxWbEBpnl1C8K0O9AQjnL3r/LyJYcxof/jQHuz7OMFdHGfZxtx+1P5ZvNBEK5XtM2cB27XM0Zov3xLuWCV6Aqgr92LvIK/n410YCFeh8iKQYYQ/NCzTUICAU7JkNdxYiSlh2BSmplEYTpzK0hIrOopnXtyrCeI013O0dstlCb5JtzBH1LWiu3RJ5ekw+sEB7Hhef+5lXTbvu+btDRldLTnLyn4GdoDuHOk+0Xn5qoIYZ6Lv719G1+AJZ7HWundlDZrurY2zBLQOqhUq9zv27uP1ZNw3cQfpRJIuje42XRolHFNz4Adea0fHEfMaiE/gDVRi06b63GJW8R7eQ8WqflumPzfxNdlb6ry2HtJzz+S6sEvKfS36QTdaNDdBzJvJenK25yz3piHrtiWWnc19xnO2e4d5zhILSLftRUPzNlQ4n2GxMHpebeMzPPVLRbkxa7xnA4rSxTuZ1bWc2E5aH7S2ol89rzKdy8RsabcX9t0MRqgZPKgcoaolgJoyUy3BIx/04ZVDQaj3vXOZHDOeIeFu7H4pBF/cO5jvwtMulH2zIaUbrYtuLODs2CzXXSuqUbeiW3Or2/P6AGq/W2S9n13ngmgz73lau9TeaKvZ7HejB1Ono1rMoJRNYcylrBTtKTPrOweO9Gc4ah/y3lwVNw725BWjdv1SuN1xJ9vO4FxzLUV0kOOo48bOZSWorQpYLJxJ9XW+7102K22feva9fgDhmD42dfEd0m/c7nGKrM8A/5pa1DzYjGKlhKVVNCb7sedlvZ8lt2/Bhuu9mgW6+4oo+n/aigG68k1eXCiuewgPf7ENu1/T74eBV1uYLDclUByduI3kGmWPEFjMBOLfCou5J9J2ISAEpiUgbt+mRSQJhIAQuIwIyDPRcbIds9qV+4vFsriutEX0pquOuWh76J/bOaNQVyxZPuDnouAUZUTf78eIsb/gtkprdmmKpIi+36e7GuPBkm9WJaX1eDmj35ZPJBWhhNEqBIC+FKBhfZZC9UA9tt1ThqnzMV0YEA1j6DejiLiWo6ysBEnxp5UOysWZ+oODCJ3h+lV+lJczsC73U2QA99L85DxGqzL9hP65x9JH1dxR5rB+Yl19AwimFBDYJY4cNQVm9j5G3UFeRbzQxHlUrQc2bkXjTfkURrDt/BelIHLo3RBcXwygbJU/tYCEjAYGRqHUIkuvK0YFrQU0EXyMFi3L8h1tt2sL3FoNDOr2Qz29o6i8czoLJzvvXK1FJ0MITU5BPRLcV8TYT/OCOYHwOPvscigSaNni/rwf/jjTOzdKq6rgc+jWXXSn995bvTgxjXLI2Yfhg106L+5Mdh3jTJlifXIY7ZryRx2LV5LGpXY89+L2p9hQj0Wt5+cYq4n9iOtyivTmrk8nTX7mnnS/dLeoKnB+nZ8Po2NHCwYycIsc62EcjBC2PrEZRc68qprwEHa1tGe0Yhs51IYdH9KNT1OCGx/W3clYW/0Z7qlIsB+tuwZR/+B2xs5IrDxVP2lJ8KJTkZDh3KTIrp5fpvJHHc75ukhRZta76G6r7XkVI8JYlqbp72zPmVl+Lr/Zti1FmdneZ1mqbTULQF0JTIs1pew1bxQqkVqpREqtStQbFgn2Ys/OIJoefwiBRCXQbK7lFP2eeLdbV/6oY+nOZYp86XYt9Ls5dnxQi6ui2lOwnhMYHJZ8oWO9GOFkiExL+Niw9S51pgseqURZpglAF8lYwNnmWa+fp+JrfTW690+g4Q6+c6OT1rsncjYPNXdVY/TVETTcRets5dpVYx1D6EPz2R5GW8uOtM2Y9RhyiY8WsBkGdkbNZyNnrdvNagytZ2vWZjnOY6aBUVpCGW+M6rvvRk3GCUlWLfbKuUn0HhlO+86JU2QdpeX+k1T4Gl2LfmZ+fLg5riqGz5rgE0PeEruKVGv+NY1odrWhZb+tBPKv2AbX/9uK18eizJ/5XRNlu/UljH0/btHrOxel+9sCbH5wc26THFI1UPYJgYuEQJqRy0XSOmmGEBACQkAICAEhIASEgBBYYALzpVhK7gYF7L/sN3Z7sfZmf3ISaw+Fpz8305Zgg+HiIjrehxcOhlG1rgplNzinW1sZ9RXGJ9hDX/Cm+L70rruTBcYJWcxN3/V+eK92fEB7qNygxZgm/ghsx4aU8Wo8CB/pRa8SIC+pwdq1ZZlE8WZV6X8pWO02rZ98Nagy6gy/S//9VKh4bixB4LTtNkUV5KKDnOFjhiBsWQFKUrgwm4pMxblMSdWAIuUqi/03CXgo1O/pU5YjIygqb0Yg1RcVGQ0xjWLkrSpGzS3TC3Bc15ahckmXJpiMDPYj9K3iOMFeqrbN9b7Qr/YyUHhq4WHn87uTqtOsMOIEhj5U/WktihwCSS3Tu1QAmfKypFLsHbHTYfT/oo0WL1kktrMZa4zbRIVq24tdaQVQzizuK+nGK1ACJMyUd6bxXOXFGGdf260pgncaYZQz/+S47lpTXZml69Zq95x5DzrTxThj2mvKv4wDQXJwKn98q2tRX11K5WkEo28dcFgFjeCFV4ewk0pae4niwIvxyp/S9ZtQQ4Wl6ywZv/4K+hk3RFuCXeh4exUab7GtCEb/ntYRDuWPr5x138q6YxEEf9ODzj5TlD+FzmfbEfhh47RWUeEjr2iz7O025rDG+79tPmNZZGxKDAMvv5BReWFmn905M0vJ5Tf7tjlLzek+8/hRV1WKIV4PqR51WrlX8vk23o9hdaOo++OcG/5l5gXNNr66L45fYE0dn4l0zUhF8RiVMR2HzGkQJ9C6fwBP3lfhaO7srmVHQXSjxckCRw6g9Q0talvcoZlvLPy7+Z1fmI5GC7BhTZHWdOUSd3jShS/QciMQiMDtVMLzVAwfNRnTZdnKguSJC59NIW8aV5sXxVgglxOlTUZJnyF0+BkrbqJ7iRvtz3HygCN5/8tUgnO/e8kU2lse047VPvgEalZ8ipD5/HSkn49V360NaLpNP8fzUb5ZZuRYJ13dmeMljtlyVf6ogtxeVKwuQZjPA328FMXYoKFsXBbgRBjjHfMZLaaWxU+eGe0zx7cc91jKH5ZJy8Gg8QLOpIj2lTdi61m6ouU40Vu+iRbULnTTBHyKzy06TzW7Oe3v1Cm6S7VSjWD4ZJQKIHP0Zx2QFSGwKAmkfYcvyt5Io4WAEBACQkAICAEhIASEwGIhEDuBQVOO6qvIOMtQuXXpMb5KAxsZL0YTsEdx+OUunOAHbntwBN7t21LH1aHyp3VnK9Uh+uJevYnC3gzKogR+0dMRRKNRhjKhNEXFdohMWB/IU6d4jIqXGK2D7IVpXJOcAW7sWRpl7BJ+ujONnsoFb44f1OFfH7AspWruNKwVTo+i9eUO/dOeM5OfbKq0m2Cs9T37qCZ09pXfgc0zFKJEHX1UMWAi4ZBVT2SCggzOVo/rv8bIIUSYYhqTn8rJ43EKNas0L2NZFaD/kDpTIxgJReGfRiBnZb1AK8lWGFOIKYuVOHlJJrENhTNvd+KVX41jSsWpOZVKPZKhcxQOde59BeOMLxQ+TmFPhqSJh5TCrbHJqTRJTMFtWi48Y7nfKaB1wvSKDrsUCr9GTVFSALW31SDru45WHQdscz0UrGvCQ7eZM7l98N1JK4kV++j2RhfqTh3twmh9GYoNy4kYhfGa8tVoTM0D21FrCfR8tNopge+lHeg0YlsMd/Yhcks9HeZx4fOiS7kiNJaSuLhNrJvu+Mq+0o0dpmskRv3q/zCKeqt8M6fjd3IIzxhtdezNenW4vdW6/7PONEcJJ3h9mjFAMhY5y3OWsew0B7NuG/PP/D7jTP26RmS+U+hS7dlB1sI7kPd/9f2P0P2bIWpiPLU+4zpT3Si9sxmN5fad4Lt2M0pWdGLXS4YA+FgfglG+D41nyKyuZdYXOzmEvfv7cPazT6mEtq9r1Za5WBb83RzuR4cxbvCtu8PgxLHA3r3GPR9A85NNSc+a0HWGssNXiS331aRX5mWAstBjgYkPhjAOf+bJLanae56ux6jE7uREgobvP4kKp0LBkd4ZN3GK75CUC/c7j0TovgyRcQyp9xyXOHdlp4fpXk+3FCy9vQk1hS4sXR73MtQzXWx/YyG0m/cf21b37QSL0Gzb6/Kh9p7Ncal7xh9FN1+DahJBQ7oxWHjAesa6yyvir12On8y7tjTTJCfWWnQr3SB/aQx5N+oKs+K11QgxFqD3ysznIDpJRY9mNedGYHUZ8g3laZTjVtNCKa5TsiEEFikBUQAt0hMnzRYCQkAICAEhIASEwMITiFH4TSHtv1Kg+0fWTrcKeV/MQ77TOiSxUUriT1dS2txhw3XGxMkTWkBxldRFn2W+a2lhMV+j0vNRTHwSQeRfI4ixzS622bN0Kbz5tBpJrFMpMSicj3EmrLX8UVdZaJqLxPRWopmtxE6OW0qZgpsyxeOJ4PArhvuhJYy7sUYXnkWPdVtCXu+aOy2BmbM1E1Qc7Xk+wSKCH7dKJJ/5k9guJXKEbqKO2NvONW2GrHNHqvVT/WjZac7uVAn02B+mgC9Vlrh950PoNIXH9MNfY1j/jP5ivyUYqKR1QvLCoPVKWDPLJX0fw5wVnN71i1ltEr8MMSd8xasATQEEvPf+JGcaZ3uWzNpm9xuoaca26hgVeC6E3rLjrVTf14y1hV4qungPXRHGKzv3aAJ595WzvykiHw3jxHFTxJNj+6ciFNycsK6DHHNPk5wu2J60Fae13/1OsmuqTCVQCBky9T++Ql25kim941h0/D3r2UCROeP0mMofO5F/TQOqD+8yngGM1zM6gWLDimfs3SE74coGh/LH3O2ii8FG9Ozcq7M7N4jgZD3KKCiN/iFoKXixrBp335psmei5sRZb1gxg7xH9vAWDVIrekNxGvbYIOv+23RCiKqsQx/PVbE6GXzUzve2oXk/uuTMUnM2hyCj2vGpYi2iWLekzzfacpS85zZEc2qZKmNV9lqYJ5u6hn/2QsUP081qwfis2OGKlgHHZ1HnTl1JscCh/zL0qllXlkn7bLZt5gL+zuZZVMVNnQggeV0r1+VgW+t0cQ9+rnUZHAti0Thd0R+LGAtXxAvQ57HbSu8xRdvr3pCNRTmMBug5lDLIuPkPbUcIYaZuTY6Q5inauRj+klaJhRdp9aBQVadypFv3JVjRX8H3HOD1nRzuwe79pJUVjFt7vyp1h6fpG1JYX0YUtJ3Jw23uti9bNQ8ZTzIvym+muzDTwpZvYAvRoz25fId2jprSO1lsa48SaT51jTasDtDg6F7GekuHRcUx8g5YzKdPqmdx0h+zNYMlqFZ1mZeDlPQ4LvQL4r52rcQcnDpl1potFzfFdW0uH1d+1axLeI0vs54eb69MtfkP5o9IVrdmAzWumy8Hjx3vw6LPdXKECa1NDsgVzFkVIEiGwGAhMfwcthl5IG4WAEBACQkAICAEhIATmkUAUo4cPoO3ggPWR5qzM7SvBHXc2oIICYucSHecs8ed0xUXB+ibcWxRKVkYYGUp5vDEHX+XOelKuU/g68IsOdPTZH/SJ6QJVtISpM1yTcaZ/S4pA170vUsBqZKzmLPoNmWa5J1YwzfbEcdP8h3FmAvaM6MRswcPtlqIHN/mh3J4NnAxh6JCpVCnApvXF8dkYjLf/7/dZQhDnwanBduwYHEDj9xpROmcf+s4aplv/Aly2RHC6xBh9vd0STtTesQpRpYT8/RDaTCsFuoSrXeml+69RBh+23cC5qOYaN2bpToZGMfr+WU4Ht6ubYjye/BtK4TesJuwj87y23OsQiMbX5VlepAnvlN7gxG/HEFtLl13xSeZ3i4Iw8y6OqRge2lKAVYxhZClMydBU1xRnuG6zbaj/5loEfkcN45KldhZaAwWzma1P91S1a0pw5OMpmLndVF9MFwPDrij92vDPGHjauH4K1m21FI/pcyQcYSwJ0wGc9wYKEBMOZ9oMB+1ng3dNwoxoK6MXq8oL0GsoDMMfqTgGysUO3VxZlkdA5ddLrBxxK1cX0koDhmXhFGM/RakA8mAyaLaaz6WqDK4bz/B+MpcMitbgm/ssd3I1DzwE76EWdNrdM0tI/cvn8j5zZrqvFg/d6UbLc6b7q9RZ5m4vrVpaDQUZxbpNf7kBPS3p49jM7pzl2urc2qZKn9V9lqF5E293oP2oodTjOfrO2kR3VY6Hbqa7QN3Axv1mVze7a1mV47mOrjUDSqlpPyEQGbHcStl15b620O/myLEuy41i4PYa5NFl5tjHIRxud4wFbi9G9GQQwY8Zo818efD3xPinegdPjeOdY0F8wfky5LvQxXgxgQWecAC2Iu1YgBbSQw4Fep7ZlyxOk+f6CqqM9PiKkcFuBOuKU06QcV2dD596//M502oqf5bx/UyrXz2WFV3nHWzD6HuV2HxvHQJU/qjna/+vzHElXWLSRVipadHMCRLmogymMy1jv9yd1t1qXL7jXXh6Z+ZnXrIr1rgSMm6EjuyzrG/0hLyX1e2sukqXn2O0RC4qTD8+zVg473frvZdqsggteDt+vJs2pPqirNNrEuLJjf2qz5gM4aVrOKu0zNUmHmU/whEX4wqlzh+1LNhTWTAnFibbQmDxEsjhMbp4OyktFwJCQAgIASEgBISAEJghASoSDvzkafSaH+IpipkKj6DjuV0Y3fgwGtckzxZXWU4cbMXTKfKau4Z5vNX9MJpSzDY302T/O8EA5k9bQsd0+YJ97fhh1IWdd6ayHknOZYi4kg/McE80YorRfSj+UuoPUyWYaD/okJYOdqBVedpxLIHb73YIN2IYe7sbba/2WkJ6lTSwbgsth7zoeWk3Y3+oPUG0/WQHKjZuRYMRQ0DtTbW4V9bj/2A8GiXQ0D4eTo9jH+N+qEuiZGMTGlbl0cWZU9rhgsd9Fj1/y7o0N3AUnn5/M/Km+HHNf0rokp/lV0h4cB+tDOyLr/u5p6HmaTqXurtqKGSgQPTFvRT5pF6mjvVg77HkY9kEaC5RjFZ5ESUA9gxnf9eD3a/qNdV9txllXqqaphz9p9TN5WAECkUf+fMK3bKM6bz5Pp1jcnMorfSjdBmF8oobLddMOUyqpPO7bwLD7xrXp28VTG9OWp1Rzlyew8o9KyrQ9GBFfInWjNz43clbHl7DmxGfO0ZB/WOa25nk9Nntib5PhbdhdQJa3d2bznVNhuKifxiz7sESzgQPj1MJORrExGmeVbcHvi8VIVAc4Ozx5JvBaYVYEliethZfgIpfUwH0uxDvroB2bWlu+LRcXhRda6r0EovxIHCTDz2H9PvrRIgKJCq4nQLZgrhgDPH5vddRKHhUt6wwVYXxKSg/PN6HVqN97tWcSX9DPnpeS0yVbpuxVV40LbDc2NREF3qnEu/+dHlnvz/0VpvldrP0LgYC9/G9kqHY2Z6zDEUnHcq1baqA2d1nSU3Qd9AN5z7TQoqq40aeo6Q32RVeS6nMMPM48n5tvIUQS4p+QOsf7V2hivXFxdmazbWsNZLP1Pqmh7RV68/5MTzzgz0OKzvrSPYrC/1upus3zU3eEjaRirLgG63Y8UZ8cwO3N2gx6YIU6rcdSTNiOcfx2kumAsPOrykR6N4x07KQY4EoJ22YdlsFVGInXVeZGnqFH2vXF2DkoCrhBHp+E0bAsJxOyhah8meXqdj1YcuWevT+xNhewmenssY53o/Wp/pRUNWErTUx9NtDEgy/F0L9jQa3yKRhPelDIN2YzmiAM9ZkUpty3JHsijW7Aibe7bTciCbloFs4pZwZ4H3pXlmHR+6rctzHSam1HeFBunN9Z1KL7aV2uMBxhMEqPPg69oXsd5Hrak7a4UQkG2UpHtqkHE0yZtjPnkLHKFDAsZDttpHuR7ltL1EMd3XgMBVU3ivtGUUqrmPpxkZUGsrMKN2h7nmuU6un8r5tqOdkIVmEwOVKIHm0ebmSkH4LASEgBISAEBACQkAIJBDgh9hP45U/bgpr71hXrvnFDn04hK43+i0h5/Bru9Htc8aaSChObS4pQO03a+nzPw9Tk6N4/SXGsDGSBd/oRvgbm6cNJp6i1LhdocMvO4RJVH7Q93j1LSXIo+uzSDiInoNdCBrCpqnBwwhuLEWAH/oVVZWI0Ff4xLt9/Gg1hCdLSlF7az7OuihQmEPrH32WvtlzNl9zjxfXDX2DwquvUuDTa82MVsI0urQzky6pQMOtFMSq4OxD/ejc3+P4oFaJClD/3c2oNKyz6hnAuOTNvdh7SFcqDbxGV14fNKD5noq0ApY8vx/5XmVZYCxeBpg2VgspMPLye1r9T1zylaBKLVQg+PPMoMD6rmz/5tN1loqHk25RM0artBmjLpSuCzB+TpCuW8zW0Xe/xU25dDH366Upn/+ez6Ur2d7v91MgyRm+ZhdjEVMUVYAAZ8aq/eYxK1feWYuR76YA3SQyRbaWRia3U2OaRVPRhfhimwxaMQ4KvhovfIt+PG5cfxRyXWeysHo+Jyv2jNyZFKcUjbNYztPF3Yum3R9Qc3+tZleTa4mT47YlzcCrFKSlKcBX3oCtdzrvv3irh9TxovTC3HTVZy2RqK4wjIYwYgnTed2ahg9WQnvF5dD2RAyLL983tmD7V43nHy3CUi9h9PzSfn6VpIrNQI5tz3YZ2UuwdZNStDtcAqUu2NobPvKKw9phKxWtzE0d1YIsJ/uxm8HEtYXxxTbdoioPW/d0chtmec6SC0y/J+e2pS9qdvcZ1Tn726z3jW8dLUqTHoSs+wofqqp8GDFiWvW++DSmqLStojXrF/jeGzt2mEok+37z8T2sx7hj3lley2l7fo7uQdMezPLAQr+b8/18m1Mg7ninxbe0AvXGBBrfqiq4j9D62nznLeW70HomJL8LwXehZgkTX2DS1kKOBULvmnYhQPlXcrdAKfp6LbwHdQu+4C84Tl1jxDhz9Cr09gG6eOy1roWaB7ai2BeG+dSqbfo+ql3vYPdPOrTr/FO+WU78xh7zqqIi7wwjspHjIK5HJ+04dK50YzpH/drqsgps/Yu18DgnkSSmSdrmc//se7OyhgzTcq/FUt4mVKCGShNjmvJHHZmi5dmu7WE8/GgD/OleCUwX+T3duWqxdFSuhOXUCcd7SSmVqlHG00qDbi4BR3w9unyja1el5Dxha4dQfR/rdjLls6Gzj+wTqlGbBU6LVOYxXxv9Lz2NouadWr0psskuIXDJE3CMGC/5vkoHhYAQEAJCQAgIASEgBHIhcFIFZrUzxAcip1x/RRHKvlaGtif3YNgQSvS80oeqv65NrUxYUYNtD/Kj3CySsX8e2u7HM3S9posSTyB8muVaCcyEufxyVuA7tmCy8t5HUH+TrbzwrfAjcEsx2ra3GG2mGEhJgjwUUtXVaxWFXKN0+6GXUdvUSJcUudSffdqY+ZG6rCTDR7UHa7duxaqYB/l5+Zqf99HXWiyrmNr76ymcDqP1sRbLTZrZghIK5O6+rTThXLhQfFsTHlnRjaeNYMWRox3YMXEWTzxYldoyJcl3uy1e7351H87eQGWeWan1G7OUbGpG50wXl78U1atDcBUGUPQl+tS/FujcxetNK9CHzX9mhyYPsF9P3uasiVZBP9qhWdP4OHO3uS7z7GZnTud6oisXu68nsPelDlrspPikomsTS3aRxM9ZeuI6rTKKaZWhSUUYt0pVlqL4xFxzvR0efc86p2Ur44VvoaOmQi5+pv5ct2E25c0GWegfX7FVjo6YU7m2Z+KkdQVkzBqmVd+OkxFsfzCF9QRzZrp7XP4iXSicsYb0BzULIuPpa6ViHDRPxngSMQzRPZ75zFdK5rLrkx/aw+2tFseaB+62hHdZnZvJITxjxv2ii8fGObEMtXo4zQrdEu3pNNLQerGxUr8F3dNkcxyev3M2+7Y5mjmr1Rit9DqOmU9DxqNJcv1mFx+oa0bj1C5apShx7RT6X2vlf/u4ueZes2XGz+mU17JZcOIvz2UOpzMxt7G9wO9mVwEVaaUIXknLQY5j/JxEM/bzp9FxVG9O9QMbrNg/Xsbo2vlkbVy7g10tdDnGZxLvpx80pxmjxeVIsZH0LrOv9LkdC4TRbygMwUkuxXEmqCnalWrX1cWoW+lGu7pGz/VjYLw23o1nLIi9TuXP/dv0WGkON25nGXfHxUlPzdsLcOAfRlBek4fuHeb7z6jUET9tcmzU2MmxmjmRI1XbnPs4+cjHsV0GvYoztb0eK5zxs3/srX3Y84bdj9K7mlFzhtZAptJb3da+Sjz5fS+eeapNf0OcG8DuHSFs2fYQipMf91q7vNdVoHR1BB5OttIXWkdzMslw0HgXBipQmcd3Gse+hdW1qFhWDBwcQ9W3aN3pUO6U3dkMLy1S1dgrGqVy8ka66k2s0+3FV1eWYML0czg1idFjJ6xxi9kCz4pKWi+Fdes5Hm1vaYP/yUbrXjHTya8QuBwIZDX+uhxASB+FgBAQAkJACAgBISAE4gkM9Rywdyjf/ikCkcNThMa/rMejLYbA7FQPXYxVpQheT/cw9zmUP2bJngA20DVHq+bmK4Jxpz91M82MfwtQutJW/tjF5MO/jL7d08hnnRMxz36mnBvl/GluV5VhzRqITxPY1nNtEcyoCrFxujIzXKK5y6mcKlSl+ND4QA12PM8Zv1xKquqx4U84izqDxUn+yloKnL1oebZTm0FZu6Esez0DhQOm2A90/5fJPaDWoFO2kEjbzuWPy4cN9zRaOZRLOHNecAmD2Acynho3XKYQJpX/eavUVCvW2Uk+6NAIRY4NpHU7Z2ak/CKnxeVwZ5JTxjlLTCXqoCkcKkWxFvfALJyCOdMl30oGv3YIbcwUi/o3NoYOQ/mr+lG70RD+59wpxp963zk3uQB199+NcsZMUu4Kw4z/1f1yG4bNWfnHu9Hxdhkab1HPKzc8SthlPJ+cRj5ZNYPu5RzZs8qSdSK6BO376X9FlyX4V24gnS4o9ZIixzotF3re8i26YDXrSiLo/Nt24xmj3IrNUFiddX3xCUepnDBjP1XeR9dvGZ8xZt5ZnjOzmGl+Z9a2aQqd0eEoel+z3fEVrK+DP8MjE7xuIs6YUWnqnOJ9EYoW2xMi5vNaTtOGXHYv7LvZhbK6RlhTHsIDaDGUP1jZMG18Qvu9QguLXDqZKe08jQVi48PWe9771bIZWWCqZhevrQLo/lUth98aRc09Fj1OrAhgy+0BKj3OYtP3voMyLR5iFGOjIU15E11eisLPG6Ro7bXhHj9i4wesdtXc14QYnxW9p6YwwJhrZRzHjv32hFYXaBmdn+27Ma1Fl15U+r8zGVdFMUSr73bzHc7CA7dv5XuHbX/zbHJVeXTL9sTD6NhJC1atnZz0sqsFjc3NKI2fF6Ll9d1Sy7ISirHcuRZg6/0NKHJyOZ+HwBfHMHyoO36iA196Hrd9lU4MdmPojAfl66tsd7RqbMjns7VkcOvopRvjLWuo8NP6PYxn9g9n7frZKl9WhMAlQCDTa/oS6J50QQgIASEgBISAEBACQmBmBCikPGlLriu/WZleDeKjG7KVB6zZwJFIig9TurkIJM7gMxrmutr+0EMWLrky98eD2u89CW3uq/NDU2WKRRGZmMTIQNes4oNkrn8ejyqXSs+ZQje6VPqWHbvIc0MttjVTieNTAubsFjUzcts2P0ZPMQZRYvDn85MYT6Mgg+Yur5ru8ljPZ5k+J3gd8BLyl9O9SXZNypwqMmwHaqZC8u5yUwLBa5WNjV3huI5YkssVxaQhXAm/O4rQzWxFwgzmKc72zaeLu8QwLLFPxhPtIqy2uT8fQPUals+ZpymudCudbruxFGV/MjPLI0dBC7vK+EX9x/UqNXdMjtpjx23BXCXjMlxqS6iv2z7vvMYqEwJSZ93f2CSiS1TMgklMLinHQw/X24Ir3g2+wlI0/vU2dD+7Cz0G6+FXDyBcxpnJV/CmcQgFHfrGLKu3n9tZZsgqWYRxWlqf12MpmBl8VVuwmYLPuIWxUfa91G/sKkUT44flsgTf3Ge58Cy5sym1W7FcCswhbfSDbgoJdcWdm0L17ONFzPacTd/Imbdt+rJzTRFjTJRu47pVFmAbqvwZiogkxxHkvVFK15I+N5XNv2YcEPN6P95LKwO6mnpis6FQmp9rOUNjZ3ZoId/NWgsn0PHfOoy20krtrgqr3RMnQ1BDMOfbUCmRQ6c+1dOE30PweCmVxAlvr/O0dskrSI5JdgHGAu8cPmz1p7LcnP5i7cp6xVNYjoolPZryYupoH4J3lWkxkswC/LfSavhWc4u/Jwexh26J1VJ3F10aJjzaXIUbsGllL62KKlG1MoDQBz700lIpeHgY0TWlYIg3bQmU6bHY9K1p/pqTVKZJNuvDymXaHsZldIzrVHzDzdPEgATdHzdsb4bnJy3GZB+OQ1tasIlKIOXCLXGJfnCAk5F6uZvX5RMPoei8eZ3RNau6z52DwXOT6DrYndKNW2K5ytFucY1DAZSYwKon8YC+XbyRrpDfYWxQtmFqsA0Hbt6WFIssdU7ZKwQuHQJ8FcgiBISAEBACQkAICAEhIAQSCUQRNWen80OuNFFBEJfcRYEmvwTpfkEtwx+EkwRn7qLsFRNxRc9kw1D8THA2cfDDIN1CjGLs+AlETCHTTMqchzzmZzHCVE5Ea6eZac7Awz99xnKpVPc9CsjYz1h0AsHfvMfg7VUoxhhaftRKv//OL+xMDad10xersfW+FAKWc1GG701YqCzRF+Uub4O5kdVvjFJsV86mDM6iGYD92TZDUEDBwlblLiuK0PvjFLRH0flcu2kw4cxkr4d7sLtFnwls79TXvHQPty3BPdyUZvmVkFKdsCvYj2uKsWFjDkJtclNC/Oy6z1gi7+r3UULtC7YZfqfXEshM/m4YY6fpfs+wJhvpNQVzfCbcmEaju2AtneuKGNfmoCHBY9EVf+qMy5NjXUpoRgFZ5sWL2ns3oe8p09olrD2jfLx9o3SRk92S6nM+Zp2/6cpwConTpqU7w75X29B11CE5ZOLKOx9GfXmi4D+KvhdNl55u1H1vU4KVWEKNCZvKrVjrIeP6Z+ydRkvJq7cuLnnOVn1pe6gfoPVXm2FFSbsmbP22LVTXEzhYpxDazu6czW/bpik958PD1nOAYlnG2As40CQWNvF2Z5yVaMn6JjSutQXktXUNCL7VhtY3TNvOEbQfDqJZszie42s5sXFzsr3A72a2eXT/HstKreaB72hjh4iynuJzI/iKskrJ1DFacTzbkjrBkhps35FgcbfQYwG6f3zdtDCk+7eymSrhtR7mo5LX0YDm2uwE+o9O0AVwvtX38Ls96B6d5AQQ/QIOU8ltLl2dHYgsd9nWzlSYxa4uRf19T+AR6ojVKKvoa3xG9FFhdKoffW+dtdzwlnw5hWbELDjxN8yYjhwfuBMVconp4rbZ3mj2cazCtIJqfSle0VJ5bzPdI2fZTsbx2tD8CNBixgQN05VaCzzbmpPdwVkDnaXwJE7CiusDN/hA/wJ/lMrd6wug4ItxT3g99WcTGNFcyGU7rtWzJf/NRz29FfQb3gp6fzmC2hsrsrd8Ty5Q9giBRUcgw6t60fVFGiwEhIAQEAJCQAgIASEwVwQ4m059lOnLCUzwAzWjHYMSjhtLqs+0vOULKCyOcAb6s4w/kVEIwsYqId4FUwo5Y71MP8s5+OYedJpCEfpm91JQuu9n/Rih8EAt3qpCfP9rkwifUtv2mdMOZvoT7qXyqSpZ+cTvcPNTvGQlhbwZ3GtkKt55zL1yE3be53DB4jyYcT2GgZ/tsawCsMyP4MFW7DsS1IQz3pWJcY4yFpZ0MNX1aieidVShh65fuvHYc6kVSHbazGul9GufKNDOmCNjbKiMOWd1MH9VDUreDGJEzZTlTP89OwdRfe9D2PBluuY6ql+r7pW8Zi6xL8no+JDl3kc97SrTBTqYFd2EzHnFqKIMrkfTrUxigrdudi7H9HJiJ4O2xZJZ9PSPEzMlwsExaz3VysT7fdjzYlf8E4UWn01NDSktOlU8o67jZknlCFwxgdBx++XgtMqjBBPh39HdkocNvlpZS0XQ/my3mRmVnJk+cTxki0UpWIyG7Gdb9OMQQifdmKIVom8FJxhMJ2y0Sk69MvTyC5YAF2vK4fokTIG62XZe7J+FYNhQ8L0RYd1heHh86XI/8jM/ROIqTHnO4lIkbyxU25JrTrGHsVP6jOeAOlq9JtPIgC6nfmkqdpSyqBGbqfxJXAK3NqLplBGjhgfDg0FEKbhXl0a2y3TXcrbl5Jpuod/NYVov7B007wM+PIIH0PI8rahUw/nOKEmhnMy6T3SNm7Qs8Fhg+BcdltKlgK5sbXVNUsuy2uG/meMlKoAUseFfDiF6ix1rLTLeT3enJsuE4oID6LXnA+gHOblmw23FyDeGs65ry1C5pItWJRH0vNFrFJDoNjWh3KRNuvJ7bCBp75zsOB1G9/5W9Bxz9tGL+gebad2aw0NLa0w+lUDNmPpRizEWC9MdXCuaHm9CwOFu2Bw3wleEPD6T7VuYsSqffNwYV05hankdtn/XfhZU3ElXoZpL44Sez8HY0yqRY+et64MYdFei4Va7buu4rAiBS5zAJTZsv8TPlnRPCAgBISAEhIAQEAILRYCBwG2VTQHypxs1TnecM1MXZImFsI8fpWYEE1VnwepqlJcUwndNPvLy8rSP955ndzhc2CxIy5IqcV1lfiqHMfZxFAEqGpIXKj9e2qG71zMVVuF+tO+PT+n7oofxbvyMeURlyNXOcujTfnDYso4pXUOrBlOmSQFs5DMGKzab4SzynHN2qUoQs4WfznQ5rE99EGKNZXEeQKbP7ui/mfjUAHqOmBucievnR/29DUnKPPcVEbTtaNGuBe+aRjTT37+p8FPH2nnMFk3a5Wlr7L9zme7ydqZNtz52nEKYBIuGpLS0tnjPNLRYsjTp8ELscOUFsHnHdsYKeIGxAk6wyin0vkz3L47K164tdmxdGqvjdEVlLu7VFZqFnbk9n7/2tTVFN020yqMJUKDYR6WQfiFET8dfi862TDnd3lzj1YVrjFehXBfpSiVacqbPjqmYHffBu8z53AASA4VTfI+aeymku4kK4TRLZNK8eFWCflrd9adJqXZTofjibu24e00Tdq63wh5p+/pfprsibS31n8hRBi0/qo650bR9Z06Ks+QSqYwK2aJKHGlDi+MZk5Sez6DWn+jXS+X921F/4yzPWVIFzh2zb5uztNmuR94fdigdK7BqGgsN5+VXuSa928jAnzBGYJ9h5bnEaOUcXcuz7XPq/I53k2qvmkwyz+/m8JF9aHnNObqh1eIhxz3HMcAdD9+NuxMntrB9wy+rcQTbuKwSzd8ja9Oil8dGeaxdHUvMx11YyLEA+bVZykVaSH49/bNGNS2rxUuXrSugK6ZZfjBSY7mV9BZWorR8UlPg9vbZz/9AOWMoGg9mN0dPvZxsoi2cyBQ/XPKi8k9L0O84J941zJtFw2KfWerkLFJnTuK+Kv7ZraWmorZlZ6s19tP20e3vw/fX2zG2Mheb4qgP9d9vRuQH5tiJk604WWTnRluZkqiIjeN1joofs1SOeZ1LLKaO2G9D61jCWMzaP8OVorWNVkzNGRYh2YTAoiWQ4g5btH2RhgsBISAEhIAQEAJCQAjMGQEPPGo2qCZbOIHhDyMIrLRVQvHVxBD6rS2ECHwlm8/f+BLmaisaGnYof3wMVvswhaGJQ15+eC6UQipDx3yBYh5VAnZO4uXM9pqUCiAX8q/JYwryjRPOuBFYXY7Smzjb9MsMOKx9/7O/99lxgbSC+adn/FEt5pFv3cNovC07gUr0D2OW4GCpix/mVxRhK12AxE4NoeXFbrNoNHz3EZTwsohOOcV8VMp4PAgN7sPeg3r/VIba+2cWC+hsxBIZWPW66S6k8ut0D7OKAcPzDOFH4mlmauWZcIToPAwm7nExnZXGDT+FQsPHrSLjVsIfmkI2r56lkDGWvl+O0FAH+2ROCy7F1u/XwzsVta0UtFIYwJhatoGXWhxKxgA2rbeFJHGVOTc+CVnCVd9NRTkqy5wFzXbdg7KND6GwuBv/9aUeW2ijFVuNypTX6mzrvJD5wxhwzAQv/2oW5ypDc8fefAZ7NMHsFCrvfYSudtLNY6elpXVfexHQApHzMr3SFpsNMSh5w03qWZG8hEdHrZ0+v8+6vK3LnEqWUSoeS1M+u+PdDRb41XNGXyLvdmLPG+Y9QBVLoAYPbam1hKJmuqTfVK4TkxKl2cF71O51mjTzttsNj3q9TWc1mqH+2Z6z9EXPvm3py879SPCdQSuTl3FP0l3ZViLHisdyD+XYaa7SukI9yZ22CurQbK9ls/i5/134d7MuJE/oiYqnxHdh5c2rEFhhnA0bmpXY5y/Q3fSSs5fvZ+OtqR33FRrHrNT2ysKNBaLobuu0KnaX16PYYVliHch5xYNV3yhF16tqukcE/ZxhUWrELfPdVIPGm7ibViZBKoC00QotlZvutC2Vo+8fsBRA1RtoGZhQv+/ra+GjAsgcAdd8I7t3h/8bW/Dw11xw0T9slPGXzMWtrGZM5Zy20w21T9XrTAfGPPRwZyymW0Ca+a1fVxFqVnvRflS/oyo2NqEho7WelTPzCt3BNdLq5xkql07QRd9DamKNY7EmFYTVhB/bklxNIKjeWIdCxo5U04um3BzfnI9XAjmKsVcX4KWwAFXY/ZE1IXABCSQ+vy5gU6RqISAEhIAQEAJCQAgIgYuHgAf+aykRM1yM9f+iH7UrE3zDm4092Y/OoP0Bm0/hwoVaJsdtl0a+dQ0plD9sWaagxgvYcM/yAGeK9miCg+C7VCokBlM32lJEFyYFvxulML4QRSuKeF788JlKj2nb6/jAzkE4a3P0oWi5fj7zfEUkCwAAQABJREFUfRQu+WrQfNckWl7VZ8t2vPg6mv7zZgRs2bHWIhXTwan8qbl/WxoF13QdcGHVumoMvTWB4pXFtJJi/5fT3dOsL7EJTH5s1E2BRPwSw/ioIc5ZVmQJvb15+Shey/gVf9iFNk1ZMIwX9vvxSFNNghA0gj66rHMGSW/aRjcpWbQ5/KEt0C8JZKesi2/73G7l857f+T0vHv2JLZgDbYG63y5D/S3Zty9ePTi3bZyT0uhKzbYGo0D1eqUNmPmSv6KQmTVxIvr/cRR1N1UmCQ5V6dEP+mzXhhTJKoGeWnzFVPgYsXCmBrsxxphTRUlf7ozNY8bLYZ6iQvMmpDXKTRToGscGDg0xJltVcv2TwzhsSi0pnCs07nPQirL95X6tHVpbqraguS61AspKZKwooWbTSgaTT2qrnsB9xVn0/HQvhk+pbS/q7mtE4dUxuJfyWqKCditjSox9HEkTL4xRMkKMZfGafqa8q+vQWF2I2GdU5i4xGjDjHxeq/mIbCscJJGXjKSI8F0Sb6Q5vWSm23FuDpbEoPNfpN/bszlmmhs++bZlKz+nY+RCGLAsN4Kur1XWeeXE5nq/DH4ZReW3qCSLhoX5LiK6sTvRlltdy5qbN+uhCv5v9qypRMgTkFZcgcH0h/FT65sdZ/GbRJUvhbKcNf/SpvrEk+cZdqLFA6K0XDKtF1RQv7l6fPJnFbnFua/krlVWObgkdPDyIyJoNrMFehv++zZp4selbtvIHkWFOeDFsX5dVY+0NKV7i0Qgm7aIQ+VeOuVQgt4wLY6Xt3YPha9ei4Zs1KHI0Jkblj4sKH2uhledE+AQmY5wgUOh4556PYOD1drz+QT4e+l5DQqw1lduFsk1NiC4PougblfDPiTLNaNXVAe1ZHfX4GD/JailXOBnsQ/29x6k3OHwsioY8M8EXsOrrZShy9i1qTqZRj900ahg1cWeelwnD2naeq5HihcAFJ2DejRe8IdIAISAEhIAQEAJCQAgIgYuLQOm/rwHoZkdbwj3Y85ofD28sjRcknqYLiJ8wCK65+GpRPo1LGDPptL/n6d7k77v5ce3RgqKX1tSj7Nrsh6/hD8c5AzHRioLC+Z/ucVgJuek6LX1LplIIS9KnzvGI071NcBDBWGXKuCouuu146MHKlIVHKLju2b8P4bKtaLrVIRxImTrbnVGMDBkf5suopEr4/vbd0oBmztxs2U9B7LkRtO56FJV3MSC8Uggw/lJnayv6LcGyG7UP/AA1qQQnZnOmOc/5KzfgoZVm4uTfiXH69/f6UZS1UoxlUGgzZpzbJEVL7ASGjuv1eFcmXj9A6Z3fR8NnT6GDM2ungt3Y9eh72PTgdxis2oMJBpF++flOS5ikgsk3PU4FWVbClyjjEZhWF4w9ZAiWk3s8yz2To+j4BRV4V6r7im527spk2UFB1c8PJFXY/+puBMc38XlQFv88oA1H8v1ExYZDCJxU2EWwIxqxLlgtjoY/4ZpP3cQYRg93YeAPMcaCicJbUovaW3ThtofKQms53om2t/zYfGuRtUtb4bOz7XlDuMgd7pU1lpLQs6KUUYh6jJg0J9BGpce2O+MFosE3X4lTWpV92a6z4CYKMU3l0PEudL5bTCsip+A9gu6X7VgbWPZVK6ZPmAonUyznXb0pa+WP6pMnz5+kDNb6av2JacoaXYXDSQY3FsUptlxeH9vhbKeVUVuJOZwreZbrCvH4FMAEA54feIdvjSspiPUwaHtd4jWamMPYdnlRdINDEpuYjLP0zYDloNWFEsY630azPWdKeJruesIs25bYlRlv87lpincp5c7iGaW7xsNx/f4KvsZ3w4rk+COx8BDa1PvEWLwrGf/HWJ/NtWyWN1+/C/5uzivF5gfjnwPOvkUnxxCKKEWB/SxwHk+9TheDY7qViK848X23cGMB/80bGHuO7nv5Xi5Y32i5aUvd5hz3egKooqFKh3qwUWOhWdiYioiTfcaEDr3M9mefQei2DfTWSheVz7VbFmm1305lwRxFz9+1xVnI9jz/Ckp/uDmjC9Ho+4ep7OKkqXA3OjgRqPm2IkQZ13GHFgPNjU3f34kyQ58f+sfd2H1Qv3/qvvcEqowxcOgfW9FxRO2nq7efFuLJ+yqSodBap/K29M/T5AzZ71HP6qSnJcdyo+bLg0UNvPQCfBtNJfEUYmrMZd7YqiqHzmf4YCeVVc4nqkrA5UyKOHf6kfi/0ajlpjiT21RERvHMrr0IL/Mij9ZwHAwibEx0iy9QtoTApUcgxR126XVSeiQEhIAQEAJCQAgIASEwAwIMmLpp9QG6kNCte8KMi/DYsRLUredswv/NxQDi/ThwaDju47f2rsq477sZ1GpnYWDdIUf8mnzO2pxOlJdHCxH1QawtwS788Nkw7vh3ZZwlS8Hg2DD6DjpmGWuJTqDvHwfgLiu1XYk5etRPN16Tq0u0D91AjgoovRGZ/tLNVnUJFThK6H8C/UcnELglC8FNLILg0SH0/LILQUNu7V3hsPTJVGU2x2gB8J6hAPGtLk3+yGcZPgbzfuRq2z2YUgj0v5pQ+JIAtnxvC4qtGaAJx83NXM8zZ8SGQ2MY/U0/en89rLnQ0mKIOPzQm0Wn+x37Z93ySh1fmvBFFA2NWkLO1DPcXai4Zxu+sLwVew+pa+0Eg9czhoJSJBpKJVWu5jbrfipXTEGT2plpofLssMEdASoDnYKSTPlyPBadDGLgqClspYDoz9IpgGIY+lkLukzrPlo91K1hLIWDet7wkXY8RsHutgdrHS1g0On2DuQ7FD4uTKDf7Jcj5cW0Gv7Qllq5byzM8hk2hbEjDCKuWbNQrsjYGiSpdct1bRUaVnbpMTe4Z+SNPXh8qAIb+Czy8VmU/OzkjHfnzPMr/KhdX4A9hgvFCAO/P36yEhvW+OHljP/xtzoZoNx2luVdUxd3vbiuLUetr0tz/agaNMAYTqHyWmz4OmOwnBrjM6cTQce1WrmxyugzlZC/MpWQ1OceHUDnVUG6GdK6lfJP9DMqRbN+NtqWolphajPh/ktZibEzLvdnqRs1+cEQho8aD8ZlDFyerQIoU8XqmDPeEtklNX2W50yVmO56mq5p07Zt2gKySxD9/ZglEMcSBnnP4hlVtLYBBYf2GM9UCtX5rBxcXYO1a4r5bjmL8aO96DJjrGjN8NFdpm1xNvNrObs+zWmq+X43p2hslK62Rv9lCAN9jG9zilflkmps37Ehy2cYxd/jg+gxnmHuqxJuxoUcCzBWz+bHm9HXF0L52gRleYp+57qrdF0dQiv9qL813mUZBzN0+TaFnjep+FYcTp1A7/7WuJh3Bbc/jBpO8Ehcgm++YD1j7WMj2P1iP55oSm31qdzQde83Ff8FbI/eV8/V+Zo+REXJGeLArsyIF+j/eh28B/dq913vW0FU3anfG35aI1cf2YVe1eZjHeh4O4CGbMaPdkPnfu2TMcfkKha/5AS6XrNVxkkV8nL91NgZDg7wvZiUIqsd0eP9aHm203o2Dby8C3kPbE898YgWnqrOqVMRWhza71CtIh+/A5JPc1ZtkETzSyDKyW5DtCB1pbTQzaJuDmJiV/pRcUtRLkOOLApeXEkSnvCLq/HSWiEgBISAEBACQkAICIE5IJBajqYVXHbPI5g4vQvd5ofZqRH6UrcFhM7aK+9qjv9IziQ1dGZMt+6YHZguSeJ+T2E5Kpf0oN8Qbk4dH0DHy7R2yLAMH+zA6Kl8K5BtQbGaPd9t5IhghEJQtbgqqIDKwQLJKCDjj29VFXxUAClx5fAvhxC9pSal4CZ2egJjH4xi6O1+DBwzhJtWyQxEvDqd9Y/ja/bK7Ib+UbqbMz/ZSzMEfFfuwR66a4zu4HhxJCg/1Ozwpkezc3vmnAVqdcm5QjdL4ZNhhI7TTde7FO6aWi9nmmnWlWArzLgmLS8PwrtkyhF3pQSlCfFsQm+bypGCjDPci2/bgk2hx/Tg2ap+h0AdtIT7AV3DOehP00JQ+HtYEyqrhNXrHG5ops2ZY4KsPqCjGKDyR1k56YsPWx5sRDGn/Jat6MGuF437g7OWW98qxibPUiPdFJWTA6YKNseGpU4+E9VmhkdaykrOnrZrycvBv6DT2inxXFfc+zBGd+7GsONZ1JnyWaSs5JqTZrwXrd2Mmnd2WW6Rpijk6tyfovmMw9B0hy0w11N4UNO0Be9xprN5L5+gZU8r/ycu3vJNdBFnzOWmW6FQ3OMliP444Xxibn07G+V86pwz32ufsfgynLF4ctIuxRczzVbqK2x254ytVc9RY0m8nsz90/+mbtt0+dLxdOaLnXOkWurN7vnGWCTf+W4dfvhcl/V8O3G0B238n2qpfWBrnDJTmQzM6FpOVbhjn61MnBkvs6iFfDcrRV/kE/UupKB9ZJiKTlq/mg3J9pdWDzG6f2x5qg3RZW5ElNLIWMpXxSteFnws4PKham2WFitXZDeeMfvmuaEK9TeYW47fK2ilVk7rS/4fe4ux297gEzNhPHPi1z0Y+FItKm602xZ+uwOtppUlSvHIk40I/exxXtfkGezE7i4frScTlE2sNjzYabn99K3bYF/reYUoY70DfF+MvENXxoYCCFcXozbgpvUSxy2DvQh9i3EPtUklXmzgO/mdXW3aNTDw6j6UrWy2y3N0caFWwxyjasuSEtT/aR46X+uPqzqaeKvRMmvT7XTxe1J3GxpL+Gawhf287ukCz+t4PpoFx04OcKJXp/VsMfd3P78DEdMy3dypfj1F2HRXPUKfOd8OMbiu9jOmpm156Mwi6xeeQOif29HZl/PTLr7hVI6XiQIonsmluxVD8EgvgpEYvCvKULnSfnhfnH022sunpGdZMap4oS6GJcYZhb2DnCXGgVrxmqo4f6aLof3SRiEgBISAEBAClyMB91VeY+Yhe++Yua+z8FL48gSKKDjs/HkvwoYwU+lmTLGBN1CBO7+5AcVGAHOToXLRo8SK2pA9qVwzlfPXjbzPq5LtJX7L3p+4ZosC8lH/aDM8r7ahx1CUuPnRaLpyc/tKcMedd+OrS4exo8V2gbTUUZGrsBpNt0+i/c0hKgqsXqLompmL5BLba23zA7iu3Iu9KqYM3YEMnaxmjAS7Nyoexz4KkZVblKRlGa2xbl+LypuK8Omxbuz7WRheuvWKX6IYMwS6yrVTBxVd8UuUbsDyUVW/AUW0TFDL8K/Mj/YAShNnvSplzPg4BU8DFAzT+kbPEq/80PaF0brjUfh4bVR8rQzFNzJukTexbWbmOE8g9k6uRce7seO51IJCldBLV1nV/66Sgo9kQYuzoCle4V4Ku9RVG3GwrLmfvvOdCamK69PcqnAnvxmKEpqsCfvocm74nSO02DoRlzNug+dyx6OHEVhdiYoKFZy7gIG3Hec1LjE36DrlsClMWlKJqkwu8xLzzvV2LIwDf9uC3uNmwW7UP7hVU/6oPd4ba/AE4wI99ZMORGj10sQZzJE3z5qJVQoU0G+geeeoA5P0se/cdiSedrXkuiRHM9PmsVzO0Uohw2VnlBPDp6fs9tuxdKatJi6BO1HB6vKjccd2uvU6gP0HB+x7xZGrgJYQd3yzNs03E93zNW+H/80OvGJZWurXsFlEYE0DNm2sSGmlB28xHtrejJ5/eAXdKa9VKo7vbER9ufM7k7NkzcJz+M1wZacvRZ2bFAK99BnijxRlc13Movz42uK33EX+NMqPWZ4zRzVJ15PjWKbV9G3LlIvOKrPgGT1tPfH5HEjHILkeT2EVdj5Op4adHWmuRU4ZoAvEhm+luRdmdC0nt8Pe49LuGfVq9GZt8Wfn1tYW+t0co6uvx1q1ySIJLdE3lxWgumotym8uSXNtmrkobF9K95/cdCp/AuuaUBn/MrwgYwGzlYm/USrp3UvoYlQpfqgACb/3nqXcdqglE7NNv80xzdixQRymBdCIcsumnhmOMYJWQHgYHS/yP8dcjfffDd/vu604iGr00tDcqMUBzN/0EEqPtmiuOcN9rXgGW/CQM37a6VGHq0Na7Kx1jlsYY5AzLAbUpIvgewifr7Csh0tuLec+NS5TVhBR+M3xgbcUTRtL0PKampTFMRdjM6Z0Bad1Yr7/0IWl6cJ2eTEq11TSAXMEu42Ybap9bXQX7PUVoKCwEIWMBZb/RR98X1mD2q96eF7dPL8uh3UG30XnVZtpE6UGD1yPUvkZzWcMSPOFw5hkbRyHmGOLwDpOyrkZ2NeiT3zQLNMPFrAt5ShZ4UfeMg7o3C7G2CtL6aIvFono7+mYssNi/eo68ObTatesULVHlgtBwHWl6YDVjYIVCQ+qaRo09fEJ/ds10dx/mnyX4uF/c/78+f81k479NqhPA/1ywPnQmklJC5Uniu4f7TDMW71o2r7tgmrHVa/DHwQx+UeucJRdWExts6bJN3lcfO3VWnY6TH/fauDHRnOWQiBB0BP94AB2GL6svWuasC0HVxxmzy/F34vlftGCEl6KgKVPQkAICIEZEvB+PkG6O8NyZprtYnk/5NL+aGQCkaiHboii4LcSvNfk2x9juRSUQ9qhlx6nlcUUqu/fjg035nDOlLLiE8bm4EdfhO4ePHQHFCeEj04g9AcKfl1LkedjP+LGYjk0cLZJI0N4fFe79hHrXrkJO+9zWH8wSO6uHa228HiJF6Vfr0FVBZUTjuA8wa4WtPbFTd3PqVWVZFuv2E4O4PGn9A9q9+pG7LwngGG6uRo/HkLwfVoGOWYL2xWwTesY/2QNYxMwvkzfQcZFcbincqYrYEydAN30+a8NUGkTb7WU6jw7x5ZaOex/yU1fRdkqKlWun064T//8LTs0Fy2+qibOxi1C6H26MOIsU9eSpfCxHfEBjDk+f7uNgh3dAqj03m1o/HJEU3SFPuKMazq3dyqPrH4tKUANFaBqgtnE6AC6DxquZKwExsoyH0qKAigs9KHg+lIUr7AVGxOs92mj3op7H2G8lkRFXWJhs92mMOZRJaiipRaVBJa7uTDPv0MxqoRbdd/9AaoSrKS02hk3Z4IztvN52QS7dvH6U98I/M56gt9ZcfKSGL/DHtO+w3J11TfbXl5M+VWcocgpXcHiupKzma+hBUXWzxzOgJ6cQJSxYLx0MxWZ8iCf+eOChWfqLJ+FExMRxOg2jVc/ljL+Qf70mrFMJV7cxyb5TH2Kz1QGbt/+19m7w5rbTs3ynM1tYy6e0mjFEj3FccSUCx4313kt53QvXCzX8kK/mxPr47O5YGUZym7iu4RKrEwTLNTJH3uT1i1qksGyGt4TtZxcEcTHpynToUVo3rVFzB/30L6gY4HEizX0Vit2v2GaoScepfLyzmZsNi1mkg+n3BOjUqf91W4MG/GpnIlK1m1CA61wvVdEOYm8G+20ZFFvt1RLDcdOtc5xaYTWVbTKsUZjK2rwyHdrqSDixIqnOLHCcLeXqs1huvrUYitqSqWdqDDl3Dz3LRwHagrL8i2MB1fsaEoEB35kuILj3hq6Pqs1FUSOVNOtWtdH4phguozmcfZ7l2GNFNjYjKY1euMn3u/Dvhe7bB5m+hn+ejmW26ZZVikXtXS9a7io1sd4hmya7nT3/Y0eT2qG1djZjPslhy8PO+8FXFuM35eZcFnXp68OTzZXZUqadCxMy74WZdm3SM9lUoccO3I9zwlPeUdJl+CqbVJ9Mdy+UQy+RN+i2uyCNAopa8bSxdBe/YKI/n4QrS/1ahupP+DsS8qjJqjJIgScBM6fQXBkTBNwLbt+Ja67ynlw+vUzH/0W45NqZs41KAksnz6DkeLMqY/x0UefaPWq2SNXMUBt4XXLss6ffcIzGP+fYzjDDO48Crim6eCZT0L46JNTervcV+GaZctxzTU5QrEal1vdVjZz5czHGPnwE21rJufmk+AxfKKeZ1mem3k7JwzQ+8nvQ/jklDoLPA9Ll2HZNeSaYFGgHUzxZ+rMKXwS/hinzqq5RG4sW7YM13xpOdxZC2hSFGrumjqjlf1JRG/bVd5reL6X4yrrWW8mTPN77gyvlxBORfR5Tqpv11znx7Is8ydeb8uM6y3XR7V5rrO5xq2eaH3neVFtV7O5lizDdartM73crYJl5UIQ8HA2nEeTWzOwu2ExMp/tiI73aMofdU/6c7XAcXngMyareFK5dfLkw79iPlufZdneMjSWd2lWQFPH2tEfprW6/u1MdxV5dO9WgrOFZVj1lQCVPrbSwFm6h8oFL19tniyfCc68UQok8q18HihnXupJs5bxiRg5CYOvcWasM4O27jasW8pRHOCMTPM5yb40NJWhnpOGRuiubfBXA5xVa4pOGED82LD2300hRtlKu9B059lzXRlq11FZc30Afj+VDYx/ksuizeBkBn2GsItB5zNPXnO5Tb4FqFrFdbo87DzYk1wlg8CXfr0ClV+jIupaW1njpQu/Jv6Phscw9JtB9L9Fv/bmbOJTZMLYJCNHgYr7KLQzS6X1z+uG8ge0/qmdd+UPhYGHO7VZykBCHI/8IssNDUNxY1PzVpT57PG92WTtl0GgzZ7nfaUWNVdGNIuseOWPniNwWx1cfAf4aDVwuS4e8tKfnTMhQIuFPDPoNp+9uRbBZ2G+Q2Gca/bFlT6Kvv2GErso23hO89HDWZ6z+WjSxVAmLTg8vJbNa9h84mbdtIvlWl7od7OH8WvW1yByTYAWpbSozSYAkwNq7LNPjS0qfbjkFwas57dxIOHnwowFEhqhbeb5C/ibTgFUitoyc8CUKnfqfS4qwMNxyh8vKtbXMzZVqTapQc9F93Br6rGNk26GDnVgyEXrXEYHaj2ot6Xm/m3xyh+ViVY5zc2b0NLSris96GpMGx9NhvCOofxRLmLvTqGwyr9WvR/VBJQpvDcepgLI6BfPvepiNzVAkUHGsaQbODu2IK0Ov12H3me7VO2ai9zaG5xWndruLP7o35fmaCmLDPFJeFmpezrCsXrFKvt85N9YheYnK7XJ76PBEQQ/HMfEJ58izElZM1pMS9vzE7REN9pMRX+T092eiidF69vgkT70MY5okOfZ7F2udXpXi1u4XJnNa3pOUup714e8LM8oDb4w8msqf2TRCKQZzQudhSBA2Z2yhORiDn/ia/V4KZZTL4lzOQ+L4guayy2Hz/C8FFJDz+fzNHNq5UTBmxhEcC7bIWUtSgJTvzuEH/2XDq3tf/aD51AXyEX6ewaHW57AP0wwe/4m7P7xf8B0uc+cGMTPXngWR8ZT4Mq/GU1/+ef4t9dPV0qKvGl2jR9qwf/1s99qR5dtfAz/5ZtfTpnykxEOHH/839MMY4tw18N/jtqbcxOQZFt3ygZRZXX4mb/Cy/+iH8313Ex9eAiP/vCneuZpzs38nZMpjPS2429eOpS6i3Tn9J8e2ISb0ijlpj45hq4X/w5v/Mv/lzJ/1T3fxz3rVnJIO4Nl6mP80/9oo5/o36TMXFj9bXz3nnW4Jl3h50/hn17772jtmln+aa833ge1t2R5vZ07hh/98Cnt1ZTpGrc7egZv/z/teO5/6BMH7P362k11f47NGyuxzBQcJyaQbSFAlyd7nuvWOLhX342yvEsXSfHGRgQG92jvhs5X+1DxYJXhCiMfNfdsnrbj/lsbse3WaZNNnyCvFNu2bUHnoU9RuUJ9KvjQcG8lnt4fRIAW64GvlNCCh8InKqIyfUi4rqaCZE2t9h9RxhX5iBZE2sd/kB/jXmx2ukXJdJ4p+Ki5LctnVFLvPKi8uwGRI6M0XM9uPJ1/E4U993vR84cSFKlnU14Ftqw5grYPgOIbiumihUo4JXybxnrC4ytC5W3qfwO7r8dsCI6OM47TCMavrMQGM+YKq4geH7YUbLX316Z255XUt5nvUG719hgCrNK71sYLAWnR0/Cfm+A6GELlRsanynSSHU3Iv7GCgjDHjrhVF+MrVCGz6i0ug2wIgRkTGHtzD7oYL0NNGmj4U1olyiIE5oXAAr+bKTMqXVs7454EqjehJso4g8sKs/ueuRBjgTS98xSuQeOdyxGjmzBr4aQ/XK3cpiV60rFSZF6h+93Ge2sY39KFynJaVK/ITz+mucKLsts2Q7PNPp+H6t92o7B+E0rTvSB9ZWh+PA8dew+j9L5aXdqXV4ZtT/jR/Wo/AvWp4wO6aBldysk8WFlKhY9TRshz/40KvDfq0iy+EkczrhVV2FTO+IgrNqBxzUyUP3xaenx0wRZF3g3V8DurzkzRPsrrpZljx46DE5a7WPsg3a5x/KT+O203YrTmmzpHK8DP6HCNv3oMILpfU5MFlUs4uwBtTe33LjfGg1f40PhEMzp+3ArfllTjJqW8q9X+q8yxKOui9XeU7t24ovwZ6ZMS1S/PvGcJj5mTdbR9/KOUWnRTJ8vFRCCMrpf3XkwNWlRtuaxcwJkuILSgtE5XBxfklNkuKS6O9mQHwemHPc7MMrvsl22qXE3z5gvUhXUBdwpdO/4T/sFQxtz1+HOozUH5cup//hz/p6E8QuG3sWf7uqRBgZPbqRGm/7GubHLuT1z/s7+iIqpk9kqgqY/6sfWxv7OKD/zHHfjr/5A8APvkn36KR59Po6SwcgOB//gY86dWIDmSaavZ1p2Yz9z+qP/v8PgL/eYmcjo3UyG88Bc/wBEzd4ZzM3/nZApvv/RXeK43tfLGbJr6vYuKx9oExeOZ4CE8bCqwnIkT1//tn2PPA5UZr7vELPjX3+L//k9P4N2kA4k7qrHzuf8d11kz8I3j5z/Ga9//K7yhFJ8ZF+b/W+ZPGCl/1Mtz+5J9btMVke31NvLzJ/A3r+lKznTXuF1H/D1v709YK6RCd/v0Ct2EXHO2KS7g5gzl/BRkuL3wrmtE422laabMzE/VUuoCEpDzvICwqXDSXDb3o+beh2htJMKNBYUvlc07Ac0d5q/z0bj1bpQmuCuf98qlAiEgBISAEBACF5jAxSJ/nCsMlgs4TpEqoNcB5akgq4XxZ8ePBXV7IXEBl17JnRXMizCRCswa/sOkrr1d4kEeNbb5RtAuTcubRZujk2GEJ+kj+Y9M/DmW8SW7jEzZI8w3YeWjyffn81P7ZtY02obW2SiQymhbA+2YCa204GagO6tuld9cjLSq3yd+P2m0WTc3z8YsOMp8kT/QjzQ17vgcNd8MHJzWJ7SqV2uoWbn+q3Y5DIM0TbnqTty++Czcok9kBoMN/ysdcijO2rnKy+zWI0W/1bkKfWKYjyac7/+/vbsBjqpM8wX+X0yLCfRtkw2tBCqxpoMETSCDyUL2kuySrNElInFAhotQgCO6wwrWMDMigkMxouJXrqKDw8TxowQKUUYQcCFbCVWJdxIGLgMkToJD7zUpEpyQDWYj3WJn2PucPp/d6U53AqhJ/4+Fffqc933Pe37ndAjn6ed9ex2SG741AU/HaVT8dgP2acGffnVEho5yHz2AZ17/MPpqXSfxS2vwR5nM+4d3yuSmI+BpPYG3XjSzbz54fgemlN+PZMvnLvoDaSXlIf12S/AnbH1PIzZbgz/Sr2XSr1R5au9pPY0P3tyCeu1Bv/u9DTg8uRxTnEFP9IMbj/bYwfX09+21avBHGcMlYpBBr6S/SuBl+3Nm8EffHOr1Kl4TT9OBgOBPqnzDbe4/3eYPppyVcYfL5d5RkimVZefTOzDZer0vSoAmIPiTjsUr5iHjJgd8HWdx+P0yyQpS6+LwFmzPSceiydEOP+hBxYuBwZ/CRSsx/ZbRsPm60FS7F28ZWT3VEqiZiNcflEk+LYu7QrKSLNfFVXQ/5k6fAMc18rn4+HeWrKBqvPBeDl6cP9Gs3XE0MPiTJBN1LylCarKkn3Y3o2Lrq6jRPpP++y1H7rcwaUg+GSLw2IEtcrzTZvsR1pp+t84I+CpF8+9VMo1ukcy9C6j/+A05d62tlh1479ht/XCNcGDuHloC8g3NlRs3Dq1z4tn0FuB17m1yFbfEf28GNm6ccRWPwKYp8O0JuEpWYmPJt3d8HpkCFKAABShAgasgMLYAy5da88giH6P9kMwBdLA1csEYKBFlUv93X6Kn/RT27NyKI2d6j+5oc+Zi8ZJ8dczl9vDn0i4PCt/duh+twal/UsXuKsCS+TOQEmKs+7ZjH8kEctVhJjazIfOOBZg9fbz6rdXzMjnas1uDJpFrx5tPPaZ1TFInn14gY3r2oLZ8Lfa4lc0urNiwFCn+q+VFxbPr/ZOoKuNyL13zQ7T9biv2N4Y4MUcmlj4sw4kE54hKi90tR7Bj2y649aei2tGNF4cL8xYuQPZYNf+zYXcZttYFHqO9phyP1ag19EnsrBlCY+5ageXTgofs8OLUoY+w9eCRkKM22pwZuHvObOQGD9OhfTPU3wMZM/XRJWn46NflaAjRf/uEEvx4YT6SLueBvgHBlYEJ+FC/9x1UfSbzlbTIvD2WB9jRtOdrP4m3363EBc8XqP+0OZoqljI+HH63zHjgj+z7sWl5gTx0VheHjBG7+tfplqyVatSfXYjpYyIEWixHCF5t2iEP0oM3hnjfUrsLRgzsZsl6WGXJepD5WB55Ph27f25mexw81oYpIbKIrE1He2xrHXNdskue17KW+nmNlDa6TuwNCLyY7QavXc1r4sHhPWaml+veVZI5NcHogCP5B3jxlnT8cmWZZl+NYy3zjOyzpv3vWIbiy5MsnIfMLBy5JqWrypG6fQM2V6r3Yc2valD62x8gmhmkuv50ADuNCw4s/mU5phn32ShM+8FPJNAkGWG/qlT7e3gr6ufnIGuk1n3Jrtr3nhlwyZq/Ho8UmVllyVI/dcwbeOI31f4KXTI2dVPpROgJbS0nDhgOSCrCxmcXmoFO5ygsWrcZo9cvM/p48Gjg/dYh13db9Wl4OpvhbomcXWUeTDLrJbD4wn6zzmLJ+JtmZPw55NzXwpVsZifV/Hsj5ktgbeCfQuvRuU4BClCAAhSgAAUoQAEKUIACFKDAkBE4sx8VR+0yOLUMFRjFSdlkKMG209r8Z8EjrURRf6gVGRIBIG9LDdZvVic9C3WBfDJRVPmzR0LtMra5/71c5mfwR1uMbdaVbnc1Nj3pxtInlsNlCQK5//0VqddXNNGHhoNv4tR/LsCTczLhPd8SFPyxHkVZb5ZsHBkULt6HbmNetG54lbtbu1pxxo3bivKnyoIbMN93Ncj+ckvwSN3V3bgHT0UaEqjLjR2vliFuzRpk2r1o+WNg8Mc8iLrmPiOdDZrIztelTrdrlJWJ2j566TlU99GUr70JuzY/hVOlK2T8UkvwSJ4KGg8G2yvw3LNGq71Wuhv347nNwAZj7PxeRbjhqgv4cLZaAisDCCr4u+ZpQ93xEwPrpec0Dh7Wq6Zj9YNm8EffClsKppemoW63+lDfo0xMb95hRrFoVjwy1NwLWnBACQqEiElqzfgkk8l8mK9kYuhBKfM4o1D8o9nYp2UvGfe8WSBgLfpjB1Qz3jTtfTUgu8TYEc3Kl414eZOWlRUpe+hqXhOZX6dOz9BBHhYXm8Ef4zQcEzFXrvcL2vU2tsvcR2frzQBjoWT+9BqCTe6LyfMeQlbl49owbh/iWOvMqAKGXS1/Mg7lKFplCf4Ym5E8eR6WZlei/Liy7QtUSdAvq0D92edrPWkZOq4I91mCP3oLo6dIttOBai2I04xjf+5CxiQ1PGWzmX9Z5S+42wz+6JXlDpwmc2PsDHO/ec4eRf1x08eoFsVKy2Ez+KQML2cGf8zKo6dINpL8XeSPkXV0+X+Ji3TPm7W5RgEKUIACFKAABShAAQpQgAIUoMDQFjDDPVXv7xjYqXb1DKzeEKo1+ANAkhnyWkDwRzJuiu5G/oQ02C51o+kPVRIhDB/YUa5lZ/2ugOCPfUIBZuXfCue1PWhurMWuygbtkkvAZdNHWLN6hjo564VT2GEJ/tjG5uLuf8iGMzEOPRLoqdm3H03a02Df0a2onbYBuSNdKJgqsZwRPpz6uNbINrJNLEC+jCrk/doOu/YErD8XZ0xOsQytk4HEBGm37kPsr2vV+uxGxfF2LNaDMz3NeMsa/BnuQvHMAmSMTQS+7ob7WJXU1b26UVHXjMzbx8B1e4F0Lg6+cw2oPalHcOwouCMX8HhhD87Y0Y5uvvTgyDuBwR+/V1EO0iRDqe2z49i/r9YIjjXs3oQK5zoUfy/ebKLXmg25d9yN7HFpsF9qR+2H76JWzwCTyPCR9nzkcVjzXmrfzIYEZP2v2Wj8P20YYQQsgXMyT47bkhERri+20RNxz5RGnDVCJPKhOF9tedAfrqbcji2NRpZNauk8uCzHt9Zy3bkKLxbIXyTX2OAY2TsUYy0bdv1iM8r1oeZuvh+rF9jw2C/MeYAC632Bs0agYhJSE0M/6rZdF3p7YFvyrl/H7lUbvs8qjYCIa/5aLE6oDJgHqHcN6xYPDv3qWc15Elav+gEOrVoXdii4q3pNPF1mBs/NE0MEOdR+J9hCuErw6KRxP6ZjSkaYvJ5hKbirNB312tw3NfVtEgAyM3GsMua6Dy0nzIDfHVPClbcha8Zs4LiaxVR/6CQ8EgBS7sgOd6PRXGppDpKNd9YV+axNm4Sd29WAafNZybrRAkDWr8WEOn1rK8q6+WuVumf0pJmYKpmxGG75fJyX4GzErDwZ3q5aDxyl4Z6C9OBDqe+Hp+NnL78M318l/Hqdw/i0hy7MrRSgAAUoQAEKUIACFKAABShAAQrEkoDNngbnWBvsw6Oe/SeA56uL3UjM1kblCtgTW2/6E2P4Tso0f/yRZeg1O+atXINs46G/EympLuT9XZVkCFWE6X87PtxmZgdl3PVjLJ5mPqhzjnUhd1IDysq2qsfpqkZFYz5mT7DD+xfJ1jFazcBPHp4N5Yvw/mVsGlxZOah6dT0qzqibTrV1Im/yeMwoHe/fEFcvASB/LGUMHpg3A2nD1HL9/X/hj9ageJw5zpuzdLkMVWdmJrk/k4NoASBv2ym06gdw5OHR1bPMPksineI1fuxWlL2vBb2+VqKkcRg/bQb8vT4TJwEg1XLMHUswY7r6TXW9ybCvnx/BLvNZJsYULcXy211Gcad4Zd+Wja0bX0ODNgRf1bs1yF9dHGbC5zFYsmY5xhun7cSsh12wl4m331SCSn+R9BOncUWMY3HlmxEYPXkmHpkceKyz1R6Zk0R9UB24J+jd8BSUPPiTwI2X7sSFpXoWRuAu67uWP5ntT83SPssyj1DH+S/g055w22Ror2QJ+jjCPO+3thd+XYY1+806LUPjejzyrwVI7q4MXxyS3fPzh5Ha7gFGpkGm/QmxSJt7Ld9oCPvUvr/HDjqUDC329pPvqBuT7sayonR4KvcGFQr/9mztO9imBbMKVzwkQ3l9gZ3hi+OqXhOHBBEefAgdF31IGHNL6DwuOd8PLEOp2XRXmVdMroaxhLwk2l5ftxnM0asbFcOsBARUJNAYdvGZQ6VBAoB6SZ/vglFloiv8z9pk10Qpp9737j+3wSfDBipt2BLNG7zqvVqUrrMMOehvOXD4vDRngnE8ZcU2OgcPLA+ck0hS+1C39o2Acr3efNmGKj37LzUPLq1ZT9c5dH2pfwhtSE4ehYSRZh97tcMNFKAABShAAQpQgAIUoAAFKEABCsSsQMrU2VgpiRRcLk9gcAeAJOvjkCUDJ2POUkvwx4SJTy3Eijvc2HRQz2wx93XW16BJfzu2BAsswR99M5wyl859eXhqW61/05E/NEkASDJfApZunJNoUJIRkFB2xiOvdBZaKk/Juh05Kdad1uHRfOhRgh59JbsozYVYbDkLAoI/ehHX3xfDXvmmP0Dl+3MLvMjs1bwzJ9sS/NFrSszkRuVBo571ZG5X1ryXzLQ5n2T+RLscr/rILCpz+DxgCf4YO+LTsOBfZ+Gxsj3qpq4qyejJR6E2D5FRTlZy71tsCf7oe8Rbspkqflvt39DUch7IYgBI1/lOvAY8Ee9njyR6Y31YH7q2dUivSXAlfoHDMkdW+X4zKGTUkwfTyx5YiMljAh96G/sjrHQcfkcbtguY+sCj/nlbfF19V0rOyMG0jNBlfJ5zOLb7VbzlHwpMKSOZE1NCP/QfyLHNo/pwbPtzRrbO0p/P9M9nE9lWa0HmdnnidfVnIaY8hHsnid/Fc31kb1zta5KAjCl55ukFrXnaG7H7tWfNodSSZmOKPg+PjAkriZdGBlGft+d1SjBRzWrxdUWnlWC9tf6qtK6HdoI6eckcqg0dHv997pB8HLclg8gR0FhQfWtEqtNjDKWWnFEgM8hVq+fXsgNPvOLDsh8WSPZZAnxdbfj4d/8bO7VAnqQNyb2paPS9+C71qeSv7OtoNoZCzJomwcXWWrz10paQQ0JOLX0Y82fm9HH/9N0f7qUABShAAQpQgAIUoAAFKEABClBgaAh4zxyXkbjkwZjtKoUsfD1IunUGZugjZQ0NtohncZU0Ix73yhSQId70LxlL2AL5tzrDtpvydwWwSwDIzNhRi3Z/rg9TA2TkpMJ3wRsQ5FBLxSFulOVBbGMd3N5cebjs9D/OUx+HteLNpx6DK6dQMoYy4RrrhD0+DvFjZU6KReEfTobtcJQ78iabWTQBVYbH4X/IBv/5yrq+xKcWY+PTxUCIbCPvhW6cb2tCxfsVevEr9OpF++fmQ8O8mXm9glHGgZy5Elz7SLKF1PLd3WbAySgj1zo73RpMM/fAeq6WzVwdAgJyz1qfp4c+I8meMLJCT+CZlSECP3rFllps/kUt7lrxPEonRX7wrVfzv3adxDO/UQONuHkhFuVZfj4EFIzwRoYg2132FGo6vkCX+cNMKl2PpRvWImNkiPqXeeyuE3uxuVrNOHHNX48pzjBBiRCHlkH8sPv5LdqeSfiF/Gzz1w7x88Ss/g1dE/OA8LUfxQvPb0VHp7hatgNFeHLDTPM+so2C62ZoQwuexr6P2/BIUYhr6WnEzv3m3xUtMm+Q5wcTzHYCjqG/sSH1lnQZ2k3NHNr5/lFMX1UQIgTkwcfvfahXkjFJT0g2kwTlhpublDV1msPQ18rmTEOWlKnXqhilEmQOrLJV+OVKdbi+Lhlm7hltqDmtqPZyvQzF9hMjUydw3wDeWQJS9ds34Kd9NFEnQc+6Y7Ox8YmZYYfv66M6d1GAAhSgAAUoQAEKUIACFKAABSgwRATaju5HQ2Pw0/sre3J2Rz4DQFeW9Cq31i0BC/0Qzmyk9JVBMyIVuTLSTFXg00B0d5lZLE27X8P63XqDfb1qwYzEbCwuqpH5g1qNwu6jVTLRe5X6frgTmd/PRvbkTGSmhg9OGZX7vSLfXh/Z10lrDSrZRdZFeVjr7YT7tBvuU6dwqqVZhqK7mh8uCaoZ7mOQGSKjx+xeHJyKVaNq2vAf7Zglw+0FLMNlzp+gh6MB+/mGAsECNxdhmZJlIPe+p/Uotm2vNAID+za9ioxX1iMjcnRJa9WD/S+VafXT8LN/LQrxUD+4A2HeX+rCyU+DgxRKWdnWfg4YHRyMuMxjf9mIlzdpwQbJhHmkyBzuMkwPAzY37X0V+7RA1T0/fwipl/M5vKLXJKCbcpE74JbgT++lQ4JCHowerV9sCdJMkiDNp2qQpn774zh002ZM18csUxq4dA4Vr+jzHWktWoZp630Mc4vjJmX4NLVtfPoGNlemiLkcz1h8aDqwBTtbjA3+FT2Ak5BobjeGrTM3RbXmkaEPjR+/YWt8gRZ3GzImBd9vYSsMYMf1KJy/AJNvkmynixfQVLkV+45r16hFAlO70/GiBNW4UIACFKAABShAAQpQgAIUoAAFKBCbAvbU7yNDviEdpz8YEYb4a+XZd5wkZ8h6j6dbRrlSl3j04PjJJnU+Y4c8bx4nD1G+1naGe1EygMYGPWcOV3YIbVfsBu3ilcmojbySdmWYs75HUYvr9bDSi5Y/DyTwod9qgOv25Vh5Uw32SNaMu8vojWp6sR0NdRX+P3DmYeWKWXBeUXFn0JBz0V1K96G3UH7QGPguukqXU0qGjTOVW9EpST1h8pbUo8h+fQkZ3nLYYe8z40CvzVcKyL127yqsvtPyYDljAibn5Eh20LPa0F/N2HmgEb+I8uFzS+UWfKA9sM//14dDZ+lECy/BzLmL5qHFZ4PvL6dRVVnrf1ivzIqyc9PjaFy0Ho8UmEGayzu2B4d+pQcyrseyVZZMmCj66/usEi/sVrNgHAUPoyT6iFmv1q/0NQk+gG30bVhcKkMGJtjQ4T6KqsNaEEbmyXl57TIs/uVmTNOG/nNNnwfXexuMYeC2Pb0MdUXzcFd2msSRTuPg27sQFJ8BvuorH8fsTYKrAPel7sA2rQF/NszRIsy9U4KRX53D4QNvoK5X40q2j7ZYgvc+/wRWlt+A9DJ9vHrcB7DiacucUlLWNaUIt8l8Qp4ztdhXrbuo91vLA0/jgYFms/XRD2V4uZ+V/QQZyo2tLRm35CBj7wa5p9Q+dO3fgaZiCcSGynrTK/GVAhSgAAUoQAEKUIACFKAABShAgSEr4Jw8A4uD5hPvPLYHb/3+PFzjb0Vxaa5MsmIuiZ8/5p8L3plzNxbcbj4/M0twTRG4ouGIb5o0/gaXXPRqNbjgTAk/rJjWsa8sD9PUTfEYP9GJ6pp29a2rGEumOeH1WiIQxklJpNH/7E32XZcClyUy4RyXj6Wr89FzoROtbS1wf+rGJyePo9UaEJJ5M8p+bce6hwsj9tM4ZMQV4zFhxJJ6gfa6oODP8DHI+8ccuG6UYNLfJiIxSebMaavC+s0VepXLfx0WZ/lwjkFSpLsu0v5e1/Hyu8gWhqiAkuViDf7op+mYgGUrivDTTZX+LS31EkyWAFCkx+u+s9X45fYTaisy/838yX0MHWcZBks/bO9XGzIK7oQ6LVARSubNw8evr8Nbh9XMiPq330BTjpqddLnHPlv9DrZ9qvZAmbNocnLv3uhbemWb+Nrw9pPvaLsL8LOFSmaLdbHIGUPwWfdb1q/wNbG0bK4OH4VpM2eq74vuxL0/PI3tGzagRsteeuulSkx+XguADU/HI0/cjxVPvmHUd1fuwMvqrWFss66kZqVFGP5NL52A6T9fC/fyDcacS12fVqJc/oRdkiYhWfuygie6qYakKYu/0bAM12cN/iQVYPXahXA59LJFKJ3djO3r16FKc6l7/Q0UT157eZldxvHNlbt++lBA8EffkzHzIRTu/jnUnNlmtMj8Rxkj9ewsvRRfKUABClCAAhSgAAUoQAEKUIACFIhNAS/qDtaiXYY2aT+XiMLbrQpeyQHSF/1Zh/6er1aBSI/arWW/e+sSWFDiMP7skvYGtFwoxnjLXNoBHe52448hxsGJu9YsVVCUh/Hfs0R2zF3+tR6v1//N7Ph4rUyPDG3mj8HYEC/z/cSNSELaOOVPNgpLZqOnuw1VO15DlVsL1MhEVm3ewoDgUdAhrvJbL44fMjN/nFMXYEVpZq8ooPnhuVLdiUe88s1vf5ytFQ2fdcMVPKybcagetJ3WAnKyzXXz1Rg6zzgYV4a4gKtoYtgH9Y6MAslEq1QzP1pO4qyvCKl9/X0hQ4G9t9YMEtwzLQ1dZy1ZiNdIFs8Z84eM7y/NOKvcypLdkzx6FGzDosAe5sC0B9dIZqI8FPc/lG9G02ddyMj4+vKO3Slz4rxdq3Xgeky7STJjLH23XQO0dJlDpp0904wOW4L8vBuB0U4H6rc/ZwQwUCrz/nSeEy89AC1oF5vNYca+6kKLDF+XIPsTklN6zWVzRa9JFKRKEZsjHYtWPYyTq15V+yke7q6ZyNIyUhJuKsBrZSmoeHcHPjCyhfTG03GfBDCmJZ/Gj1dv8W+0OfoRpJB5eB749cvIOrALO3dXm07+lq5H/vyHcO80B7Yve1w1Tk4Ic8+Gvzl9kqlUr3dXXv1XpvWkFlhRdshQhevuhys4u2ZkGuavX4tmCVC5/fVPo+msB6k39eP8/PX6+l86JluH1AsoKoG6+emo2n7av7XR/QWKr+ixAw7GNxSgAAUoQAEKUIACFKAABShAAQoMJgF53l+tPWob84/ZlgQD9ST0wEZ75SZsvZCL+BAPtb1fdyMpYwZmTI7dZ8y602C69GZf4xORJt+UbvdnhLSj4g/NGD89dLpXc90hyzBkZhNOl3z3vlINOFS/ewjTV88InaFz/gjWPrtLregsxrqVhWg7WIbyGnVws8IH16E4KHgUZ09B8dLlaHusDGbYxTz2N77Wcx7NxvNpJ2bd1Tv4o/SpU7KYruwSj5QbJUFPm2eo9t9qUTyhOLTz57XYowfMpBNJerDtynaIrQ1hAT0soZzixJv6yNCRgIz5mPsCfJcioPg8UAdAU8t98OLj+KCPKspwbU/4Ez2ux+rNL0mwqQ0Vuw6gQzqYnDVTMi3C9W0Upt85SR6Kn/C3frLlC5TKmImXc+zULzssgYcv8MLan/fRc5kr7Tcb1OBB0jxser4AHZ+ZwSHsfhaP7e6jesuHeGa1Os/QPY9vRolLAmOW4lf0mki7nrNHsbviJHwXbcgqnYfJzjCBkuRJuOdm4C1/FpQE5857JABk3gFKkKjkwbUoWSJDx0nqjcf3NWzXjYBDy0jpOLzXPAvrCZlbw6/ZHJgy8375sxCeL6Xtr6Rt2wgk2BPUwKBkiNbptb9SAzg2yepxWeYn8niUg4Y5t0uWDsn8Rr1K3Xxn7+CPfjwJUN1TkoYX9lvvMH3nFXhNzTEymkK1ZpMgo754jKCivoWvFKAABShAAQpQgAIUoAAFKEABCsSqwKlqc4SqW8eN6ZOhoe5I2P22hPyYDgBF8530sHjf/o4k5M9UB09S+tJ68DVUhJjTp/vPFXitsjVkd+O/l4NMfU9XNV7ff0p/Z3ntwZEPtYnTZav9e2n+4IXdYUYOq3YfgjaKjqWerJ5vR+gjW4tJylqkh8/W4gNdD7ja59HWZs5lpDfp/Y8avLLbEq66NnyM0CsTZ0W7ZP5ToVm0vQqv7W6wpOlpuy648dZL+81yEmjLGRv++GZBrlFAF9AemmtvT352Tt/R+1U+c+YIWymQ6WL6XobZLAGjvouG2us7exI7K6tRVV2NulYjEhuqaMBzfn+3LvPYGG6ZfCX0EfvYKuctI0MOfLmK10Q61XHigN+05nAlznZbAiFRdtjX0Qb3ZzL8WOs5eJSfwzJ0X4LDgeTkUUbwRwnJuP9QbbR4W0a44J1RRF25eA4tbmn7szZ0aQGchJFa2xJ80rPCzjadNCq6pqQb95kSJNKXmqbwAZoOa/3UlN4BoMSAW0pv0nh1GMPCGZsua8WWnGbO89ZyFB19DNvp810wjjUq0QzIGRu5QgEKUIACFKAABShAAQpQgAIUoEDsCbQfwdajauKFcvIVMqpK7yfZKovN6ULe1LyQf3Jz8jA7JyX2/CxnPOifriuTQ2W+34QG7aSqfvsUGiYWojhXmR/Ii1NHDqHqZF8hGCeK52Si4X21hdaaN/HYyQwU356DNKcdPR1uVB+skOGCdDU7ZhXJ1/Flcd6aDexTB85BezWee6wJBXflw3VDkgyr5kXbp8dlfqEGM/NouAtOywhzZvikG28+9woys+Rm/NqO4rnFcF6NKzNMMqbkObB6Lj7s3/w02u+4G9lp8nRX5i9qOFaD2kY1G0o/2/ajNTh+kw3jx/WeY6m77k280pWJFHlCbc8olowGMyCm1zdenXmYN/Ej7DipPqBtr9uKtY0ZKLkjDynXx6HdXYuPKhsCMgWK5+aFzhIyGuUKBXoLJI9T5qdRh5RyV/5fdN2ZhlDhj47j2vBvShM3pyM5IEDau13YUrDs5edxVuYp6TVHjlbcc6YSz/xGnd8lteghLJWMRJ88/E5WojiWOYFa5C+tjpJwx/SgvlbN/lGavW2cBBtkOLbLObZtdB42PZMG6bq1G1qv5UXOveXQFpRXqkGGwgdWYXqqA75hkqUi4YQp/7IZqRIgCV1Z6n/VjPKnt6BFaTG1CD97oAgOyeawSTaKsly1ayJtW1jxQeUJyTjK8x+z1/88p1Hjz/5R9qTD5VT7drb2OTyzW81wyl/xMhZNCuIXkJwAAB7CSURBVHG3dDVi53G9xTxkjYkuUOE7W4dfPq1ljhasxOuLJuqNWF7P4fBefXg+IH+S+UtJcoZSXr0XuvYfwNmZEzDaHxG0VMc5HNLnpZLNE1Kv13ZaCh5uxNkH8jA65D2u1Fc/L/6Kf7W2PcD14Sm4LVX+rvHfEKdxTP7SSb0lhKsMq3joPfPYrrF63wd4XFajAAUoQAEKUIACFKAABShAAQpQYAgIdGPPb3cFPCfGmQqUve/EGnmOH7wkZhVj1u2hRwULLhuL769GmOGbdRzmxIJVC1D27FZ1ihk5evvJKmyVP9EuzpwFWPKXV/BmTatapasJFRJUCrXkzl2KTBnNzL8k5mLFXZ9g0z69bDuq9+1CdaiKEo6a/eMSy1iF8Rj//TGoOqgd86LMjXNUWXci756+A0A9fXybOuShjY3xyLkrD1Xb9IeNPhw5uAvhE+SkYlcDdvy2GUvXrfHPXRSfMh7SayOrqbWxwb9ud+RBeo3eaT3GwZE9/1F0XngKFVrMDOK8f6duZ5ZT1vLmrkThWEu0TOJG/f9uf2CbfBcbAgmpE5GFHeqcKJ0fYvPeCVgtD86ti6/jKDb/xvykTi2YYGZNyEPpj3fLUG0yNJXnKyDrn2SuGG1YMdvIUUgNnkfF0rAP5sN72w1pGD3afA/nBEyVsupQX9XYvGMiVs/PMY/rb8eHpr1l2GkEKgCHXX2Qf1nHlrYTnCmQZ/LhlxuUh+9qAGh0ajpGj1GP668gAajRN/XxF6lkzhiP969LgWtMYBbKZV8T+fQ3VR/AMRk/L+GrC3DceiemT1KzcJJvKZIuakGEw1uwPUfmtZlscVdOwHcO+195VpvnRtkgcxNdp7wqwSmlvhqkqdm0A9NeeQgBU9ZI3e0byswh9AokkGKhQV/3S3K639wfB6kuw6FpmzE9sHEc2/4q9hnpo0XIsDRuG229Z06gfG8jfvGDwHu5pXKrZa6fdGS51Cthc0qQS85M/XFbjfIw91v977ZY6qchVQvaKTYDXxKQNX0Sdr6tBq/2vfgKMsrWIsO4SZSWfajf/SpqjIMUBJy7sZkrg1qgp/0Uqj7xIS8/E/Ywv3F6uztx/tx5xN0gX5Ixk94G9Xmz8xSgAAUoQAEKUIACFKAABSgwcIGG9zehVkvGsE0owQKXG2/K8/fuo1vx1IUSLJ2bL0kWlufGX/cM/GAxUDPMP8cH2ZknZmLlupWo+uBdVITK9nG4MG/hLHT/2yvY759fxvr0Tj3X8SXLsfJ7NdizW8n26R1qsI/NxZw5MzD+RsvNJVVTpi3GozfUYtf7H4Wsp7Q+RjKS7p5ZjDQ9cKQeEmn/cB9K/nMXKurd/iwBdXPvvsl33LUayovMpzNOAi1HlUwd63ZLkaBVW0qikUmTlDULK++Lx9ZtVVrATGlDP18bMvLvxg9Lvo9T29cb2ToBzcWl4b4flWDb+xVotTjFX6uWsiXY/b3yt6htM+vbUbh0A9KOVmDP3mpt7ib1LPQe2F3iPLO3M2S+J+W5Zrvy4Zd5n6JZkm4IAo+mEssMfgHJ1LnvwQI8pgV43DJnzQN/KsLSmXlIlcSNlqZalL+nZumoJ5uHkhzLkF4y10/NfjM7KPnv75SAUnSftQC84PlMpF8lD0xC3evqQ/GWylfx4z8XYOm9RUhNlFhry2l8fOAd1PmjBWpLjpJVmBJuTpuAgwW9CT520O5Ib33++v0457/qn2BpWYJmvZbLvSbS4NlDu1Cl2aQmFUgASD2KbXQOlmZL9pKWoVP1q8dxeso8zC2Q7CsJTPk6GrHv7Q/V7CStY3f9dJ6R8ZXguk0Cc7u0wFwtnlneiLvmL8DkWySI1NWMD57fogYT/XWvxyOlQVk8fd0vI9NxRzaMvm17ehlOlizEHZMnSMCsC8f2bsEHx7/QegUUrrgbycY7WRkm98wiuWe0QErL/mfx09bZmPs/R8ORMAIt1Tuw87A5NFxq6Wy49J+PYr5Y6j6h11Xutz9Own1z7kTGWDl6RzMO796CGsv95rr3fmREl9xk7WXI9dHTFiBfjq0GeE7jhZWLkX/vQ5iWIYFEjxxbzr3KEuic+sCdYTKUQjbPjYNEoPnIflTVtMuXXZzyRZKV/i+SWLve01KB9ZvVL+zYpi7Fk8qEZ1woQAEKUIACFKAABShAAQpQIGYF3IfKLUO/ObF4joy2NUKGcXOvx65GH7ob96Ns/X6MmZABn/J4XBavzwvvpR55ehcn/8kyTPmfZZHnQ8YSvM/YMXRXhkYASLk+8U4Uzl+OwrledHZ24ysl8hcXB7s9CfYR2mkufRL5fVxL54R8LJU/PRe60dkt8/L0aG04pI348FRJ4/KwdHUeerxe/Fe3jE0oEcieCzIq4fB4JDnsiAt3Yw2TOYzmLJU/oToVj8KVG1EYYlfmnJXYGLKOpXCcC8s3brRsMFedkha3cmMxvDI/kTfOjviebhmmTvqaKH3VimXPfxKphe34SgziHIkBQ9cljcvH8tWhJeNuzMOTG8MMweRvOw6unBlYKX+Ub/12e+Pl+nihsNmTkxCeOQmzVm/ELPM0Qq7FpxZjo5wbl6EpICOYaYvPH7YMF6JInrIQy2RelM3V2sP1TytR/qI16KO3k4Zlz9wfmNEhn9cr8QzcEhLRD4bReTIs3KePo1zvV4tkZrxYbewPWMle2CvbI2B/H29CHbuP4ld4V+hrc1nXRHpo0zJ2lM4GXncZou7Bp9G07HEjm6Tl8A68cDj0aWXNX4vSWyxXWAIl859YiLon39EqfIF9SlZOiOp3rViDrIAsFinU5/0iffvRWtQt32AEker3v4P6/b0bTy1Zifkhhp8bXfAQ7qtdhm1asKTr+C4joBTQStLdWFYSmB2k1F36/yz3W+cJbPuNGoAMqKu8mfIQHpHhEvuz9HmfDRuF+c88hJOrt0ioS11q3pOAU4gDKOe+KC8oaytEOW4abALtqJHgj38Znia/R/Xuf1xqNnKHV+GIZDb7/ngcnRIAuqwpx3ofglsoQAEKUIACFKAABShAAQpQYJAItH1cjvKDbqO3BT9aKsEf5W0cchc9Dtvu17GjrtW/v7XRHFWqW6YaWa8OuWPUDbeSt2gNZk2IraQB/Xl/OJPBtz1OAhnWiXYGcAZxI+wyDEn/b4Q4Cfwk6eln9v7XH0BXL6tKfKJTywyKNzKErA0m3SiZRldxiZfgXLyfSY7PYV+uovR3sWkZHq3fE77boOTpKH8NpGalRQjS2DB50fP4RVYltv1qh2XoL9Miq2gh5pYWIdKIV+Hm+zFbCr2WHrLhBExZ9BJSsyvx1tZ34DaG/rK0kSpZGqU/lAyXgT8QD31syzH6XE1D8khLgKTPsr13OsalhLk2l3lN+uqSzDmzqHwzJlfswluS3aUHHKy9S80uwj0/mB1y/p6Em4rwWlk6Du1+BzurteHkLJUdNxdg7g9nY8pNwdEfSyFttdf9kpCOR379Mo5V7sLm90IE+5LSMXfOQhRPCRd8ScD0VeUYXbkD5dv1c7tejmZmDk0tfRjzZ+aEcNfut9xq7HzvDdS39O4vkuR+WzCw+21iyHvcPIZN5n57sSxFJmoM7Xo5xzaPwrWrJ9CDzs9bce68ktanBHbld5wbU5CSGCKSE6IT3sZa6L+Oj/nHHGWQ2BCLE9nTnDhSKYGii8fRcn62fBklRDFuogAFKEABClCAAhSgAAUoQIEhL5CYojwbUQNAGaUrMGOc9fl6PLJLl2P8PzSj4fhxHP/EjZYz7ca4VtHiOKP8N2207Q2Gcn9z6dKl/x5IR0+71YuR7uJwHQPxY53YEviufF66v5TMtBhcPF3n4PFomSm2EXAkOWCTzI1ve/F5POj68gJ8MoSa7RobEhzXI2F4YH7Lt93Hq3X8gVyTrhPv4KebKpF673r8oo9sFc+XXXK9FVfJFlKut0Oud7SsFz3o6LoA28gR8Mm1SRgp1yQh2soRtC750NX5BXzXjYBN5jJSXvsXbPPB0/UFPNdIn/4qrz7lXk6I+l72ybl1Xa1zi3Dq0FyVe13J4UqwXw/HyCvkGunYEfbbR0YX0IjQzIB3f1f+frCeQPPRj7D1/WrJDu692GRI3MWLZLhB6+/hvYp5UVW2HhX+BCA7ljyxBuPDfNGj5/MarH1JTYvLlPn/FkwOHSrqdQhuoAAFKEABClCAAhSgAAUoQIEAge/ivy8DOhjFm7a6PTh+bR5mRPlvw54eL3zeHhllSp55yohgXnn2oi/+0b2UN/J8SFl6euKQOiEtZCKEWmJw/L+/13noZQANjuvEXlKAAt+gQIJjlARXvsEDRnkoW0ICkuVPLC79viYyb8x7EvxRlvTRSgZM+CVhpEMCNwO84MPlmji1a3Klr80wGxzJ2nxTA8qyUoKEci/7T11/De8QvMd2Nc8t+GDB763HDt7H998pgVO7y/BmnT9yE7JfvjNHUP5UM5asWYnxYYJA3pYaLfgj4b6JJUhsq8Bjv62CLVSA+6L5y3nDzjI8sSdUYFAC+KOKse7hwkH/i3pIVG6kAAUoQAEKUIACFKAABShAAb9AytRZ6M+YOHEyGpjMcCKjTIX5BypdjSlfSEEBClCAAhT4jgr4cOj5dfAP5ypz3RSHmCvnO9pxdosCg0qgs35XQPDHPqEAc6bnYJT8Ht12vAZbDx7Rzqcdb/7uCDYuyg1xft04tK3K2D7jn7PR8/un/O99lmCPUSBoJWyZc8pQdFwoQAEKUIACFKAABShAAQpQgAIU6I8AM4D6o8WyFKAABSjwrQk4pizE6kVFSP7WesADU2AIC1xqw65teoAHcN2xFEunm8P8Jk2fjXWuRKzfXKEiNNbB3ZMLV9Bvku1Hd6Fam4jLNmE28mROn86bi1Gc8FX0QzEGMft8PiSlZTP7J8iFbylAAQpQgAIUoAAFKEABClCAApEEgv7ZHqk491OAAhSgAAW+aQEbpj/+FqaHGhnqm+4Kj0eBISrgdR/RptqUE3QUYoEl+KOfcnxqHgqGV6D6orLlv9CjvFp/k7xwSuYOatKLI396pn89aVwuCscZm6NbudQDDLM2Hl01lqIABShAAQpQgAIUoAAFKEABClDAFOC/rE0LrlGAAhSgwHdVgMGf7+qVYb+GiEDLiQbjTPJK88Nk28Sj+NE1mPq1D7Zr/wfsI4wqsuJF1Ztvwjp7UI/EcKxLe2MtKj4+ivgJd2P2tDTrrqD1HtRuXos9ZwD7hFlYuSgvTH+CqvEtBShAAQpQgAIUoAAFKEABClCAAgECDAAFcPANBShAAQpQgAIUiDWBTjTUd2sn7UTmuHj/es+FTnSe74b3a3l7bTyS/tYpQR87kgICP2q15kNbUSEBm/CLF8d370GDMjzcmeMolgBQ+Ck6/wtt59SWuiXFSO1N+Ja5hwIUoAAFKEABClCAAhSgAAUoQIHQAgwAhXbhVgpQgAIUoAAFKBAbAt7zaPYP6yan68yGvdst8wG9hSNnfL3O3+7Kw4J5s5Bmid50Nn6E1w66e5UN3BCPbBlWrmq3lLvYgObuWci0tBFQ9sI5NGn9cbnGBOziGwpQgAIUoAAFKEABClCAAhSgAAWiFxgWfVGWpAAFKEABClCAAhQYcgIyxKIxymJ7BcqeLQ8Z/FHOu9tdi9eeegrH2/Xx3XrwSWW1RmJD8aJ5yAgD5PxetranG8c/6wxTSgaTa3NDz0dypSaGLccdFKAABShAAQpQgAIUoAAFKEABCvQtwABQ3z7cSwEKUIACFKAABWJDYHjgaTonFmLBoiVYsmgBCidaM3G6seNX+7UgTRxu/ftMf8XMOT9B4YREI3gT2Jq8c6ZBLQmcqm/rtVvf0PZZk7Y6BmmjOACc7sJXClCAAhSgAAUoQAEKUIACFKBAfwU4BFx/xVieAhSgAAUoQAEKDEUBZdg1JQgkrwWLHsWMCUnGWY6fkImcrI/w3DYt2+diLSrq8zE7KwlJ4/NRUpqH/BwpL8PJhV9kfqGJNjSc9MF36hQ6JRxkHkGv5UVzfbv6xjEeKYz/6DB8pQAFKEABClCAAhSgAAUoQAEK9FuAGUD9JmMFClCAAhSgAAUoMEQFJPhjz1kSEPzRzzQpawbmSQBHX5patGDPiDTkT3Wpm83derGA15Sbx6vvLzahTR/nzVqipx2faPEf+0QXGP+x4nCdAhSgAAUoQAEKUIACFKAABSjQPwEGgPrnxdIUoAAFKEABClBgSAt8f3Jq2PMbPzXf2Nd9qhle4110K85ULVAkA8W5P+8dAeqR+X9ataYyb06JrlGWogAFKEABClCAAhSgAAUoQAEKUCCkAANAIVm4kQIUoAAFKEABCsSigBOuG/rIu7EOHnyxp/9AyWnQQ0DuT7VUH0srrac+0d7JcHFj++iHpQ5XKUABClCAAhSgAAUoQAEKUIACFAgtwABQaBdupQAFKEABClCAArEhEJeC8Q79VNvh/ksfeT09lqBPsh0RRnzTGzVfhyUiY6z6tr1XBpEXp45q+T+OTM7/Y6pxjQIUoAAFKEABClCAAhSgAAUoMCABBoAGxMZKFKAABShAAQpQYKgIxMM10WmcTO0xt7EevHK8strY5LwpBdaEIGNHnyvxSB2vHavdjfOWeBIutOBIl1p5TM54zv/TpyN3UoACFKAABShAAQpQgAIUoAAFIgswABTZiCUoQAEKUIACFKDAkBZIm5RtnJ/v6Fbsqe89PFv7sV3YY4kN5WaNMer0Z8V5U4ZWvAWtnWbNzlNHZGYgdckeYNtma1yjAAUoQAEKUIACFKAABShAAQpQoP9f3KQZBShAAQpQgAIUoMCQEogbW4DZrgrs0gI8tdvK4J5YgOJcJRNHhmb7fQWqGy1BobElyLtxYL9Gxt+Y6h86zgcf2rpkuDmnMtdPDz75fYNmmonxA2x7SF0UngwFKEABClCAAhSgAAUoQAEKUOAyBQb2L/fLPCirU4ACFKAABShAAQp8lwTikLtkBU6t3QQ9DNN+shpb5U+vxZGLlf+SH/3wb5d60HNJbyUOccOSMH1qAezjb5X5gGz+fb6OBlSf0cpMyESSrPZY5xuS93Fx/LVVV+QrBShAAQpQgAIUoAAFKEABClAgGgH+SzoaJZahAAUoQAEKUIACQ10gLgULnn4UR/7tQ+yqaQpxtnZkFpVg9u3Z/Zifpx1bHy8zgkpGo8NtQF2I4JJSoHEH1j62wyiqr2SUrsTiqeZcRfp2vlKAAhSgAAUoQAEKUIACFKAABSgQWoABoNAu3EoBClCAAhSgAAViT0Cyc3JLFiP3n73o7OjGV1/3iIFk7YyIhzPR3k8PCfL0dKM5VK2LvlBb+9zm/lyZIYgBoD6RuJMCFKAABShAAQpQgAIUoAAFKGARYADIgsFVCgx1gbi4a2RInb8O9dPk+VGAAhSISkD5mcgljMCweCT55+YJs7+PzWZoR9binJgzZza8wyQYdDnLJZ80xeDP5RCyLgUoQAEKUIACFKAABShAAQrEngADQLF3zXnGMSxw7bVxDADF8PXnqVOAAoECys9ELldaIA5JDju8XwHX+XntGJ+Te6UPwvYoQAEKUIACFKAABShAAQpQgAIUiEKATz6iQGIRCgwVgWuGDUNCwnB8LUP6MBNoqFxVngcFKNBfASXzRwn+KD8TuVxhgbg0LF695go3yuYoQAEKUIACFKAABShAAQpQgAIUGIgAA0ADUWMdCgxiAeWBZ/x11w7iM2DXKUABClCAAhSgAAUoQAEKUIACFKAABShAAQpQIJIAv/oaSYj7KUABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoMAgE2AAaJBdMHaXAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABCkQSYAAokhD3U4ACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFBJsAA0CC7YOwuBShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFIgkwABQJCHupwAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAKDTIABoEF2wdhdClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKBBJgAGgSELcTwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUGmQADQIPsgrG7FKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUCCSAANAkYS4nwIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQoMMgEGgAbZBWN3KUABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEAkAQaAIglxPwUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhQYZAIMAA2yC8buUoACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFIAgwARRLifgpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABSgwyAQYABpkF4zdpQAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAKRBBgAiiTE/RSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClBgkAkwADTILhi7SwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUiCTAAFEmI+ylAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKDAIBNgAGiQXTB2lwIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAAQpEEmAAKJIQ91OAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKECBQSbAANAgu2DsLgUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSIJMAAUCQh7qcABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACg0yAAaBBdsHYXQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABSgQSYABoEhC3E8BClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFBpkAA0CD7IKxuxSgAAUoQAEKUIACFKAABShAAQpQgAIUoAAFKEABClAgkgADQJGEuJ8CFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKDDKBv7l06dJ/D6TPp93ugVRjHQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABShAgQEKpLtcUdVkBlBUTCxEAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKAABQaPwIAzgAbPKbKnFKAABShAAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUCC2BJgBFFvXm2dLAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBADAgwABQDF5mnSAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQGwJMAAUW9ebZ0sBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEAMCDAAFAMXmadIAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAbAkwABRb15tnSwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQAwIMAAUAxeZp0gBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEBsCTAAFFvXm2dLAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBADAgwABQDF5mnSAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQGwJMAAUW9ebZ0sBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEAMCDAAFAMXmadIAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAbAkwABRb15tnSwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQAwIMAAUAxeZp0gBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEBsCTAAFFvXm2dLAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBADAgwABQDF5mnSAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQGwJMAAUW9ebZ0sBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEAMCDAAFAMXmadIAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAbAkwABRb15tnSwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQAwIMAAUAxeZp0gBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEBsCTAAFFvXm2dLAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBADAgwABQDF5mnSAEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQGwJMAAUW9ebZ0sBClCAAhSgAAUoQAEKUIACFKAABShAAQpQgAIUoEAMCDAAFAMXmadIAQpQgAIUoAAFKEABClCAAhSgAAUoQAEKUIACFKBAbAkwABRb15tnSwEKUIACFKAABShAAQpQgAIUoAAFKEABClCAAhSgQAwI/H+3OV6UY4FDjAAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 步行路径规划\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C-1 准备base url、params、response.json（） "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C-1 准备base url、params、response.json（） \n",
    "def walking(origin,destination,sig=None)->dict:\n",
    "    url = 'https://restapi.amap.com/v3/direction/walking?parameters'\n",
    "    params={\n",
    "        'key':key_xu,\n",
    "        'origin':origin,\n",
    "        'destination':destination,\n",
    "        'output':'json'\n",
    "    }\n",
    "    response = requests.get(url,params=params)\n",
    "    data = response.json()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C-2 准备walking 参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C-2 准备walking 参数\n",
    "龙岗社区居委会 = geocode('广东省广州市从化区龙岗社区居委会')\n",
    "龙岗社区居委会_location = 龙岗社区居委会['geocodes'][0]['location']\n",
    "中大南方_location = 中大南方['geocodes'][0]['location']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C-3 路径规划"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C-3 路径规划\n",
    "中大南方_龙岗社区居委会 = walking(中大南方_location,龙岗社区居委会_location)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': '1',\n",
       " 'info': 'ok',\n",
       " 'infocode': '10000',\n",
       " 'count': '1',\n",
       " 'route': {'origin': '113.679287,23.632575',\n",
       "  'destination': '113.669129,23.600956',\n",
       "  'paths': [{'distance': '4569',\n",
       "    'duration': '3655',\n",
       "    'steps': [{'instruction': '向南步行161米右转',\n",
       "      'orientation': '南',\n",
       "      'road': [],\n",
       "      'distance': '161',\n",
       "      'duration': '129',\n",
       "      'polyline': '113.679592,23.632088;113.679609,23.631003;113.679609,23.631003;113.679609,23.630877;113.67964,23.630638',\n",
       "      'action': '右转',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '向西步行99米左转',\n",
       "      'orientation': '西',\n",
       "      'road': [],\n",
       "      'distance': '99',\n",
       "      'duration': '79',\n",
       "      'polyline': '113.67964,23.630634;113.679362,23.630521;113.679201,23.630473;113.679201,23.630473;113.678711,23.630365',\n",
       "      'action': '左转',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '向南步行237米左转',\n",
       "      'orientation': '南',\n",
       "      'road': [],\n",
       "      'distance': '237',\n",
       "      'duration': '190',\n",
       "      'polyline': '113.678711,23.63036;113.678711,23.62934;113.678711,23.62934;113.678707,23.629132;113.678668,23.629058;113.678576,23.628989;113.678424,23.628902;113.678368,23.628841;113.678333,23.628776;113.678303,23.62865;113.678277,23.628372',\n",
       "      'action': '左转',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '向南步行498米向右前方行走',\n",
       "      'orientation': '南',\n",
       "      'road': [],\n",
       "      'distance': '498',\n",
       "      'duration': '398',\n",
       "      'polyline': '113.678273,23.628368;113.678572,23.628338;113.678685,23.628307;113.678685,23.628307;113.678845,23.628234;113.679045,23.628073;113.679158,23.627964;113.679158,23.627964;113.679214,23.627869;113.679236,23.627778;113.67928,23.627526;113.679288,23.627322;113.679288,23.627322;113.679253,23.626853;113.679184,23.626623;113.679141,23.626497;113.679002,23.626259;113.678941,23.626176;113.678711,23.625977;113.67862,23.625864;113.678529,23.62572;113.678438,23.625191;113.678429,23.625056;113.678451,23.624939;113.678572,23.624657',\n",
       "      'action': '向右前方行走',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '向西南步行715米左转',\n",
       "      'orientation': '西南',\n",
       "      'road': [],\n",
       "      'distance': '715',\n",
       "      'duration': '572',\n",
       "      'polyline': '113.678572,23.624653;113.678559,23.62451;113.678416,23.624323;113.678416,23.624323;113.677582,23.623793;113.676532,23.623346;113.676363,23.623255;113.676272,23.623151;113.676111,23.622834;113.676042,23.622739;113.67579,23.622483;113.675499,23.622279;113.675417,23.622174;113.675386,23.622088;113.675378,23.621836;113.675378,23.621836;113.675391,23.62161;113.675434,23.621476;113.675543,23.621359;113.675764,23.621181;113.675846,23.621089;113.675903,23.620994;113.675907,23.620855;113.675877,23.620725;113.675747,23.620547;113.675304,23.620052',\n",
       "      'action': '左转',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '向东南步行406米向右前方行走',\n",
       "      'orientation': '东南',\n",
       "      'road': [],\n",
       "      'distance': '406',\n",
       "      'duration': '325',\n",
       "      'polyline': '113.675299,23.620048;113.675642,23.619991;113.675825,23.619922;113.676901,23.619193;113.677261,23.618785;113.678069,23.61783;113.678069,23.61783;113.678181,23.617656',\n",
       "      'action': '向右前方行走',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '沿乌土街向南步行1380米右转',\n",
       "      'orientation': '南',\n",
       "      'road': '乌土街',\n",
       "      'distance': '1380',\n",
       "      'duration': '1104',\n",
       "      'polyline': '113.678181,23.617652;113.678095,23.617205;113.678051,23.617075;113.677973,23.616923;113.677743,23.616558;113.677609,23.616289;113.677391,23.615816;113.677339,23.615668;113.67714,23.614826;113.677049,23.614323;113.676927,23.613359;113.676845,23.612786;113.676602,23.611489;113.676445,23.610603;113.676445,23.610603;113.676415,23.610438;113.676411,23.610265;113.676411,23.610265;113.676489,23.609848;113.676502,23.609622;113.676493,23.609236;113.676415,23.608746;113.676415,23.608485;113.67648,23.60819;113.67661,23.607734;113.676819,23.606736;113.676862,23.606541;113.676914,23.606415;113.677014,23.606254;113.677535,23.605673',\n",
       "      'action': '右转',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '沿乌土街向西南步行165米直行',\n",
       "      'orientation': '西南',\n",
       "      'road': '乌土街',\n",
       "      'distance': '165',\n",
       "      'duration': '132',\n",
       "      'polyline': '113.677535,23.605668;113.676988,23.605373;113.676402,23.605;113.676163,23.604887',\n",
       "      'action': '直行',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '沿934县道向西步行118米直行',\n",
       "      'orientation': '西',\n",
       "      'road': '934县道',\n",
       "      'distance': '118',\n",
       "      'duration': '94',\n",
       "      'polyline': '113.676159,23.604883;113.675929,23.604831;113.675599,23.604783;113.675599,23.604783;113.675013,23.60474',\n",
       "      'action': '直行',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '沿桃园东路向西南步行673米向右前方行走',\n",
       "      'orientation': '西南',\n",
       "      'road': '桃园东路',\n",
       "      'distance': '673',\n",
       "      'duration': '538',\n",
       "      'polyline': '113.675009,23.604735;113.674236,23.604683;113.673893,23.604618;113.673464,23.604492;113.673464,23.604492;113.672457,23.60421;113.672457,23.60421;113.67168,23.604006;113.671541,23.603958;113.671411,23.603854;113.671068,23.603472;113.670807,23.603264;113.670807,23.603264;113.670525,23.603073;113.6702,23.602904;113.6702,23.602904;113.670022,23.602795;113.670022,23.602795;113.669909,23.602682;113.669818,23.602517;113.669787,23.602413;113.669718,23.601901',\n",
       "      'action': '向右前方行走',\n",
       "      'assistant_action': [],\n",
       "      'walk_type': '0'},\n",
       "     {'instruction': '沿桃园东路向西南步行117米到达目的地',\n",
       "      'orientation': '西南',\n",
       "      'road': '桃园东路',\n",
       "      'distance': '117',\n",
       "      'duration': '94',\n",
       "      'polyline': '113.669714,23.601897;113.66964,23.601771;113.66964,23.601771;113.669488,23.601558;113.669488,23.601558;113.668997,23.601072',\n",
       "      'action': [],\n",
       "      'assistant_action': '到达目的地',\n",
       "      'walk_type': '0'}]}]}}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "中大南方_龙岗社区居委会"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C-4 实现路径规划"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instruction</th>\n",
       "      <th>orientation</th>\n",
       "      <th>road</th>\n",
       "      <th>distance</th>\n",
       "      <th>duration</th>\n",
       "      <th>polyline</th>\n",
       "      <th>action</th>\n",
       "      <th>assistant_action</th>\n",
       "      <th>walk_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>向南步行161米右转</td>\n",
       "      <td>南</td>\n",
       "      <td>[]</td>\n",
       "      <td>161</td>\n",
       "      <td>129</td>\n",
       "      <td>113.679592,23.632088;113.679609,23.631003;113....</td>\n",
       "      <td>右转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>向西步行99米左转</td>\n",
       "      <td>西</td>\n",
       "      <td>[]</td>\n",
       "      <td>99</td>\n",
       "      <td>79</td>\n",
       "      <td>113.67964,23.630634;113.679362,23.630521;113.6...</td>\n",
       "      <td>左转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>向南步行237米左转</td>\n",
       "      <td>南</td>\n",
       "      <td>[]</td>\n",
       "      <td>237</td>\n",
       "      <td>190</td>\n",
       "      <td>113.678711,23.63036;113.678711,23.62934;113.67...</td>\n",
       "      <td>左转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>向南步行498米向右前方行走</td>\n",
       "      <td>南</td>\n",
       "      <td>[]</td>\n",
       "      <td>498</td>\n",
       "      <td>398</td>\n",
       "      <td>113.678273,23.628368;113.678572,23.628338;113....</td>\n",
       "      <td>向右前方行走</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>向西南步行715米左转</td>\n",
       "      <td>西南</td>\n",
       "      <td>[]</td>\n",
       "      <td>715</td>\n",
       "      <td>572</td>\n",
       "      <td>113.678572,23.624653;113.678559,23.62451;113.6...</td>\n",
       "      <td>左转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>向东南步行406米向右前方行走</td>\n",
       "      <td>东南</td>\n",
       "      <td>[]</td>\n",
       "      <td>406</td>\n",
       "      <td>325</td>\n",
       "      <td>113.675299,23.620048;113.675642,23.619991;113....</td>\n",
       "      <td>向右前方行走</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>沿乌土街向南步行1380米右转</td>\n",
       "      <td>南</td>\n",
       "      <td>乌土街</td>\n",
       "      <td>1380</td>\n",
       "      <td>1104</td>\n",
       "      <td>113.678181,23.617652;113.678095,23.617205;113....</td>\n",
       "      <td>右转</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>沿乌土街向西南步行165米直行</td>\n",
       "      <td>西南</td>\n",
       "      <td>乌土街</td>\n",
       "      <td>165</td>\n",
       "      <td>132</td>\n",
       "      <td>113.677535,23.605668;113.676988,23.605373;113....</td>\n",
       "      <td>直行</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>沿934县道向西步行118米直行</td>\n",
       "      <td>西</td>\n",
       "      <td>934县道</td>\n",
       "      <td>118</td>\n",
       "      <td>94</td>\n",
       "      <td>113.676159,23.604883;113.675929,23.604831;113....</td>\n",
       "      <td>直行</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>沿桃园东路向西南步行673米向右前方行走</td>\n",
       "      <td>西南</td>\n",
       "      <td>桃园东路</td>\n",
       "      <td>673</td>\n",
       "      <td>538</td>\n",
       "      <td>113.675009,23.604735;113.674236,23.604683;113....</td>\n",
       "      <td>向右前方行走</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>沿桃园东路向西南步行117米到达目的地</td>\n",
       "      <td>西南</td>\n",
       "      <td>桃园东路</td>\n",
       "      <td>117</td>\n",
       "      <td>94</td>\n",
       "      <td>113.669714,23.601897;113.66964,23.601771;113.6...</td>\n",
       "      <td>[]</td>\n",
       "      <td>到达目的地</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             instruction orientation   road distance duration  \\\n",
       "0             向南步行161米右转           南     []      161      129   \n",
       "1              向西步行99米左转           西     []       99       79   \n",
       "2             向南步行237米左转           南     []      237      190   \n",
       "3         向南步行498米向右前方行走           南     []      498      398   \n",
       "4            向西南步行715米左转          西南     []      715      572   \n",
       "5        向东南步行406米向右前方行走          东南     []      406      325   \n",
       "6        沿乌土街向南步行1380米右转           南    乌土街     1380     1104   \n",
       "7        沿乌土街向西南步行165米直行          西南    乌土街      165      132   \n",
       "8       沿934县道向西步行118米直行           西  934县道      118       94   \n",
       "9   沿桃园东路向西南步行673米向右前方行走          西南   桃园东路      673      538   \n",
       "10   沿桃园东路向西南步行117米到达目的地          西南   桃园东路      117       94   \n",
       "\n",
       "                                             polyline  action  \\\n",
       "0   113.679592,23.632088;113.679609,23.631003;113....      右转   \n",
       "1   113.67964,23.630634;113.679362,23.630521;113.6...      左转   \n",
       "2   113.678711,23.63036;113.678711,23.62934;113.67...      左转   \n",
       "3   113.678273,23.628368;113.678572,23.628338;113....  向右前方行走   \n",
       "4   113.678572,23.624653;113.678559,23.62451;113.6...      左转   \n",
       "5   113.675299,23.620048;113.675642,23.619991;113....  向右前方行走   \n",
       "6   113.678181,23.617652;113.678095,23.617205;113....      右转   \n",
       "7   113.677535,23.605668;113.676988,23.605373;113....      直行   \n",
       "8   113.676159,23.604883;113.675929,23.604831;113....      直行   \n",
       "9   113.675009,23.604735;113.674236,23.604683;113....  向右前方行走   \n",
       "10  113.669714,23.601897;113.66964,23.601771;113.6...      []   \n",
       "\n",
       "   assistant_action walk_type  \n",
       "0                []         0  \n",
       "1                []         0  \n",
       "2                []         0  \n",
       "3                []         0  \n",
       "4                []         0  \n",
       "5                []         0  \n",
       "6                []         0  \n",
       "7                []         0  \n",
       "8                []         0  \n",
       "9                []         0  \n",
       "10            到达目的地         0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0               向南步行161米右转\n",
       "1                向西步行99米左转\n",
       "2               向南步行237米左转\n",
       "3           向南步行498米向右前方行走\n",
       "4              向西南步行715米左转\n",
       "5          向东南步行406米向右前方行走\n",
       "6          沿乌土街向南步行1380米右转\n",
       "7          沿乌土街向西南步行165米直行\n",
       "8         沿934县道向西步行118米直行\n",
       "9     沿桃园东路向西南步行673米向右前方行走\n",
       "10     沿桃园东路向西南步行117米到达目的地\n",
       "Name: instruction, dtype: object"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# C-4 实现路径规划\n",
    "df = pd.json_normalize(中大南方_龙岗社区居委会[\"route\"][\"paths\"][0]['steps'])\n",
    "display(df)\n",
    "df[\"instruction\"]\n",
    "# 请思考，是否可以转换成语音或者地图？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学生练习：公交路径规划 \n",
    "#### 本节关键点：参数阅读，获取数据的表格化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# D-1 准备base url、params、response.json（） "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# D-2 准备integrated 参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# D-3 路径规划"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# D-4 实现公交规划"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 学生练习：驾车路径规划\n",
    "#### 本节关键点：参数阅读，获取数据的表格化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行政区域查询\n",
    "\n",
    "* 学生操作为主"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 搜索POI\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 关键字搜索\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# E-1 注意函数默认参数可留给使用人选择\n",
    "def keywords_POI(keywords,city,types=None,citylimit=None,children=None)->dict:\n",
    "    url='https://restapi.amap.com/v3/place/text?parameters'\n",
    "    params={\n",
    "        'key':key_xu,\n",
    "        'keywords':keywords,\n",
    "        'city':city,\n",
    "        'types':types,\n",
    "        'citylimit',citylimit,\n",
    "        'children':children,\n",
    "        'output':'json',\n",
    "    }\n",
    "    response = requests.get(url,params=params)\n",
    "    data = response.json()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>parent</th>\n",
       "      <th>childtype</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>typecode</th>\n",
       "      <th>biz_type</th>\n",
       "      <th>address</th>\n",
       "      <th>location</th>\n",
       "      <th>tel</th>\n",
       "      <th>...</th>\n",
       "      <th>pname</th>\n",
       "      <th>cityname</th>\n",
       "      <th>adname</th>\n",
       "      <th>importance</th>\n",
       "      <th>shopid</th>\n",
       "      <th>shopinfo</th>\n",
       "      <th>poiweight</th>\n",
       "      <th>photos</th>\n",
       "      <th>biz_ext.rating</th>\n",
       "      <th>biz_ext.cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B00141IHRZ</td>\n",
       "      <td>B0FFIGLX5N</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学广州校区南校园</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>新港西路135号</td>\n",
       "      <td>113.298415,23.096714</td>\n",
       "      <td>020-84112828;020-84036491</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B0FFIGLX5N</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山大学</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>113.297228,23.091182</td>\n",
       "      <td>020-34250646</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>4.4</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0FFGKLYFZ</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山大学(海珠校区)</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>滨江路新港西路135号</td>\n",
       "      <td>113.303932,23.094743</td>\n",
       "      <td>020-84112828</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B00140BD3O</td>\n",
       "      <td>B0FFJGE87R</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学广州校区东校园</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>大学城外环东路132号</td>\n",
       "      <td>113.391786,23.067691</td>\n",
       "      <td>020-39332007</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>番禺区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B001403454</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山大学广州校区北校园</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山二路74号</td>\n",
       "      <td>113.289848,23.128255</td>\n",
       "      <td>020-87333018</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>越秀区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B0FFJGE87R</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山大学</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>大学城谷风路与贝岗村大街交叉口东南100米</td>\n",
       "      <td>113.391350,23.060548</td>\n",
       "      <td>18127840848</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>番禺区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B0FFK8L6A0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山大学北校区</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>东风东路658号</td>\n",
       "      <td>113.289856,23.128307</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>越秀区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B0FFGY3Q2M</td>\n",
       "      <td>B0FFIGLX5N</td>\n",
       "      <td>101</td>\n",
       "      <td>中山大学(西侧门)</td>\n",
       "      <td>通行设施;临街院门;临街院门</td>\n",
       "      <td>991400</td>\n",
       "      <td>[]</td>\n",
       "      <td>新凤凰十六巷</td>\n",
       "      <td>113.293891,23.097407</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B0FFI5XZ5T</td>\n",
       "      <td>B0FFGKLYFZ</td>\n",
       "      <td>101</td>\n",
       "      <td>中山大学(小北门)</td>\n",
       "      <td>通行设施;临街院门;临街院门</td>\n",
       "      <td>991400</td>\n",
       "      <td>[]</td>\n",
       "      <td>园东路尽头</td>\n",
       "      <td>113.303903,23.101376</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B0FFHNI4GV</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>国立中山大学</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>大学城外环东路与逸仙大道交叉口东南100米</td>\n",
       "      <td>113.391895,23.071001</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>番禺区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B0FFLQ4AEQ</td>\n",
       "      <td>B0FFJGE87R</td>\n",
       "      <td>101</td>\n",
       "      <td>中山大学(东门)</td>\n",
       "      <td>通行设施;临街院门;临街院门</td>\n",
       "      <td>991400</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>113.391402,23.060487</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>番禺区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B0FFHY4FBH</td>\n",
       "      <td>B0FFJGE87R</td>\n",
       "      <td>101</td>\n",
       "      <td>中山大学(西北门)</td>\n",
       "      <td>通行设施;临街院门;临街院门</td>\n",
       "      <td>991400</td>\n",
       "      <td>[]</td>\n",
       "      <td>大学城外环东路132号中山大学东校区中山大学生活区内</td>\n",
       "      <td>113.388403,23.061703</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>番禺区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B00140TVB2</td>\n",
       "      <td>B001403454</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学中山医学院</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山二路74号中山大学北校区</td>\n",
       "      <td>113.290365,23.127024</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>越秀区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>B00141RGVQ</td>\n",
       "      <td>B0FFIGLX5N</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学管理学院</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>新港西路135号中山大学</td>\n",
       "      <td>113.292377,23.093221</td>\n",
       "      <td>020-84112602</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>B0FFHK2VJ7</td>\n",
       "      <td>B001403454</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学-医学部</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山二路72号附近</td>\n",
       "      <td>113.289338,23.127305</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>越秀区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>B00140MR9A</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山大学南方学院</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>温泉大道882号</td>\n",
       "      <td>113.679262,23.632583</td>\n",
       "      <td>020-61787326;020-61787333</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>从化区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>B0FFGAQ4CK</td>\n",
       "      <td>B0FFIGLX5N</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学-中文系</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>新港西路135号中山大学东南区274号</td>\n",
       "      <td>113.299193,23.094370</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>B0FFF3UNRS</td>\n",
       "      <td>B0FFK8L6A0</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学护理学院</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山二路74号中山大学广州校区北校园</td>\n",
       "      <td>113.291335,23.129996</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>越秀区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>B00140A6B6</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>中山大学新华学院</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>广汕一路721号</td>\n",
       "      <td>113.388118,23.195184</td>\n",
       "      <td>020-87065915;020-87065995</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>天河区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[{'url': 'http://store.is.autonavi.com/showpic...</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>B0FFF9YJU5</td>\n",
       "      <td>B00141IHRZ</td>\n",
       "      <td>309</td>\n",
       "      <td>中山大学南校区公共实验教学平台</td>\n",
       "      <td>科教文化服务;学校;高等院校</td>\n",
       "      <td>141201</td>\n",
       "      <td>[]</td>\n",
       "      <td>新港西路135号中山大学园东区106号楼</td>\n",
       "      <td>113.304094,23.095864</td>\n",
       "      <td>[]</td>\n",
       "      <td>...</td>\n",
       "      <td>广东省</td>\n",
       "      <td>广州市</td>\n",
       "      <td>海珠区</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id      parent childtype             name            type  \\\n",
       "0   B00141IHRZ  B0FFIGLX5N       309      中山大学广州校区南校园  科教文化服务;学校;高等院校   \n",
       "1   B0FFIGLX5N          []        []             中山大学  科教文化服务;学校;高等院校   \n",
       "2   B0FFGKLYFZ          []        []       中山大学(海珠校区)  科教文化服务;学校;高等院校   \n",
       "3   B00140BD3O  B0FFJGE87R       309      中山大学广州校区东校园  科教文化服务;学校;高等院校   \n",
       "4   B001403454          []        []      中山大学广州校区北校园  科教文化服务;学校;高等院校   \n",
       "5   B0FFJGE87R          []        []             中山大学  科教文化服务;学校;高等院校   \n",
       "6   B0FFK8L6A0          []        []          中山大学北校区  科教文化服务;学校;高等院校   \n",
       "7   B0FFGY3Q2M  B0FFIGLX5N       101        中山大学(西侧门)  通行设施;临街院门;临街院门   \n",
       "8   B0FFI5XZ5T  B0FFGKLYFZ       101        中山大学(小北门)  通行设施;临街院门;临街院门   \n",
       "9   B0FFHNI4GV          []        []           国立中山大学  科教文化服务;学校;高等院校   \n",
       "10  B0FFLQ4AEQ  B0FFJGE87R       101         中山大学(东门)  通行设施;临街院门;临街院门   \n",
       "11  B0FFHY4FBH  B0FFJGE87R       101        中山大学(西北门)  通行设施;临街院门;临街院门   \n",
       "12  B00140TVB2  B001403454       309        中山大学中山医学院  科教文化服务;学校;高等院校   \n",
       "13  B00141RGVQ  B0FFIGLX5N       309         中山大学管理学院  科教文化服务;学校;高等院校   \n",
       "14  B0FFHK2VJ7  B001403454       309         中山大学-医学部  科教文化服务;学校;高等院校   \n",
       "15  B00140MR9A          []        []         中山大学南方学院  科教文化服务;学校;高等院校   \n",
       "16  B0FFGAQ4CK  B0FFIGLX5N       309         中山大学-中文系  科教文化服务;学校;高等院校   \n",
       "17  B0FFF3UNRS  B0FFK8L6A0       309         中山大学护理学院  科教文化服务;学校;高等院校   \n",
       "18  B00140A6B6          []        []         中山大学新华学院  科教文化服务;学校;高等院校   \n",
       "19  B0FFF9YJU5  B00141IHRZ       309  中山大学南校区公共实验教学平台  科教文化服务;学校;高等院校   \n",
       "\n",
       "   typecode biz_type                     address              location  \\\n",
       "0    141201       []                    新港西路135号  113.298415,23.096714   \n",
       "1    141201       []                          []  113.297228,23.091182   \n",
       "2    141201       []                 滨江路新港西路135号  113.303932,23.094743   \n",
       "3    141201       []                 大学城外环东路132号  113.391786,23.067691   \n",
       "4    141201       []                     中山二路74号  113.289848,23.128255   \n",
       "5    141201       []       大学城谷风路与贝岗村大街交叉口东南100米  113.391350,23.060548   \n",
       "6    141201       []                    东风东路658号  113.289856,23.128307   \n",
       "7    991400       []                      新凤凰十六巷  113.293891,23.097407   \n",
       "8    991400       []                       园东路尽头  113.303903,23.101376   \n",
       "9    141201       []       大学城外环东路与逸仙大道交叉口东南100米  113.391895,23.071001   \n",
       "10   991400       []                          []  113.391402,23.060487   \n",
       "11   991400       []  大学城外环东路132号中山大学东校区中山大学生活区内  113.388403,23.061703   \n",
       "12   141201       []              中山二路74号中山大学北校区  113.290365,23.127024   \n",
       "13   141201       []                新港西路135号中山大学  113.292377,23.093221   \n",
       "14   141201       []                   中山二路72号附近  113.289338,23.127305   \n",
       "15   141201       []                    温泉大道882号  113.679262,23.632583   \n",
       "16   141201       []         新港西路135号中山大学东南区274号  113.299193,23.094370   \n",
       "17   141201       []          中山二路74号中山大学广州校区北校园  113.291335,23.129996   \n",
       "18   141201       []                    广汕一路721号  113.388118,23.195184   \n",
       "19   141201       []        新港西路135号中山大学园东区106号楼  113.304094,23.095864   \n",
       "\n",
       "                          tel  ... pname cityname adname importance shopid  \\\n",
       "0   020-84112828;020-84036491  ...   广东省      广州市    海珠区         []     []   \n",
       "1                020-34250646  ...   广东省      广州市    海珠区         []     []   \n",
       "2                020-84112828  ...   广东省      广州市    海珠区         []     []   \n",
       "3                020-39332007  ...   广东省      广州市    番禺区         []     []   \n",
       "4                020-87333018  ...   广东省      广州市    越秀区         []     []   \n",
       "5                 18127840848  ...   广东省      广州市    番禺区         []     []   \n",
       "6                          []  ...   广东省      广州市    越秀区         []     []   \n",
       "7                          []  ...   广东省      广州市    海珠区         []     []   \n",
       "8                          []  ...   广东省      广州市    海珠区         []     []   \n",
       "9                          []  ...   广东省      广州市    番禺区         []     []   \n",
       "10                         []  ...   广东省      广州市    番禺区         []     []   \n",
       "11                         []  ...   广东省      广州市    番禺区         []     []   \n",
       "12                         []  ...   广东省      广州市    越秀区         []     []   \n",
       "13               020-84112602  ...   广东省      广州市    海珠区         []     []   \n",
       "14                         []  ...   广东省      广州市    越秀区         []     []   \n",
       "15  020-61787326;020-61787333  ...   广东省      广州市    从化区         []     []   \n",
       "16                         []  ...   广东省      广州市    海珠区         []     []   \n",
       "17                         []  ...   广东省      广州市    越秀区         []     []   \n",
       "18  020-87065915;020-87065995  ...   广东省      广州市    天河区         []     []   \n",
       "19                         []  ...   广东省      广州市    海珠区         []     []   \n",
       "\n",
       "   shopinfo poiweight                                             photos  \\\n",
       "0         0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "1         0        []                                                 []   \n",
       "2         0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "3         0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "4         0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "5         0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "6         0        []                                                 []   \n",
       "7         2        []                                                 []   \n",
       "8         2        []                                                 []   \n",
       "9         0        []                                                 []   \n",
       "10        2        []                                                 []   \n",
       "11        2        []                                                 []   \n",
       "12        0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "13        0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "14        0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "15        0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "16        0        []                                                 []   \n",
       "17        0        []                                                 []   \n",
       "18        0        []  [{'url': 'http://store.is.autonavi.com/showpic...   \n",
       "19        0        []                                                 []   \n",
       "\n",
       "   biz_ext.rating biz_ext.cost  \n",
       "0             4.5           []  \n",
       "1             4.4           []  \n",
       "2              []           []  \n",
       "3              []           []  \n",
       "4              []           []  \n",
       "5              []           []  \n",
       "6              []           []  \n",
       "7              []           []  \n",
       "8              []           []  \n",
       "9              []           []  \n",
       "10             []           []  \n",
       "11             []           []  \n",
       "12             []           []  \n",
       "13             []           []  \n",
       "14             []           []  \n",
       "15             []           []  \n",
       "16             []           []  \n",
       "17             []           []  \n",
       "18             []           []  \n",
       "19             []           []  \n",
       "\n",
       "[20 rows x 21 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E-2\n",
    "df = pd.json_normalize(keywords_POI(\"中山大学\",\"广州\")[\"pois\"])\n",
    "df"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 周边搜索\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "* 注意：只要参数需要location，都需要将用户传入的值先进行地理编码操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# E-3 \n",
    "def around_POI(location,types=None,radius=None,offset=None,page=None,extensions=None)->dict:\n",
    "    url='https://restapi.amap.com/v3/place/around?parameters'\n",
    "    params={\n",
    "        'key':key_xu,\n",
    "        'localtion':location,\n",
    "        'types':types,\n",
    "        'radius':radius,\n",
    "        'offset':offset,\n",
    "        'page':page,\n",
    "        'extensions':extensions,\n",
    "        'output':'json'\n",
    "    }\n",
    "    response = requests.get(url,params=params)\n",
    "    data = response.json()\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'status': '0', 'info': 'INVALID_PARAMS', 'infocode': '20000'}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "around_POI(location=\"116.473168,39.993015\")"
   ]
  },
  {
   "cell_type": "code",
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